486 lines
22 KiB
C++
486 lines
22 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 20.04.2018
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//
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#include <ops/declarable/helpers/transforms.h>
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#include <helpers/Loops.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) {
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const T* x = input.bufferAsT<T>();
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T* z = output.bufferAsT<T>();
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const Nd4jLong* xShape = input.shapeOf();
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const Nd4jLong* zShape = output.shapeOf();
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const int rank = input.rankOf(); // both input and output have the same rank
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const int rankMinusOne = rank - 1;
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const auto zLen = output.lengthOf();
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if(mode == 0) { // CONSTANT case
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const T padVal = padValue.e<T>(0);
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auto func = PRAGMA_THREADS_FOR {
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int zCoords[MAX_RANK], xCoords[MAX_RANK];
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for (auto i = start; i < stop; i++) {
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shape::index2coordsCPU(start, i, output.shapeInfo(), zCoords);
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const auto zOffset = shape::getOffset(output.shapeInfo(), zCoords);
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memcpy(xCoords, zCoords, rank * sizeof(int));
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bool within = true;
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for (int j = rankMinusOne; j >= 0; --j) {
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if (xShape[j] == zShape[j])
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continue;
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const auto left = paddings.e<Nd4jLong>(j, 0);
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if (zCoords[j] < left || zCoords[j] >= left + xShape[j]) {
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within = false;
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break;
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}
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else
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xCoords[j] = zCoords[j] - left;
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}
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if (within)
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z[zOffset] = x[shape::getOffset(input.shapeInfo(), xCoords)];
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else
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z[zOffset] = padVal;
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}
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};
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samediff::Threads::parallel_tad(func, 0, zLen);
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}
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else { // REFLECT and SYMMETRIC cases
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const Nd4jLong shift1 = mode == 1 ? 0 : 1; // REFLECT : SYMMETRIC
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const Nd4jLong shift2 = mode == 1 ? 2 : 1; // REFLECT : SYMMETRIC
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auto func = PRAGMA_THREADS_FOR {
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int zCoords[MAX_RANK], xCoords[MAX_RANK];
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for (auto i = start; i < stop; i++) {
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shape::index2coordsCPU(start, i, output.shapeInfo(), zCoords);
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const auto zOffset = shape::getOffset(output.shapeInfo(), zCoords);
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memcpy(xCoords, zCoords, rank * sizeof(int));
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for (int j = rankMinusOne; j >= 0; --j) {
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if (xShape[j] == zShape[j])
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continue;
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xCoords[j] = zCoords[j] - paddings.e<Nd4jLong>(j, 0); // are ready to fill middle (within input dimension range)
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if (xCoords[j] < 0)
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xCoords[j] = -xCoords[j] - shift1; // means fill from left
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else if (xCoords[j] >= xShape[j])
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xCoords[j] = 2 * xShape[j] - xCoords[j] - shift2; // means fill from right
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}
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const auto xOffset = shape::getOffset(input.shapeInfo(), xCoords);
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z[zOffset] = x[xOffset];
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}
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};
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samediff::Threads::parallel_tad(func, 0, zLen);
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}
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}
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// //////////////////////////////////////////////////////////////////////////
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// template<typename T>
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// void pad2_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
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// const int rank = output.rankOf();
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// std::vector<int> dimsToExclude(rank);
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// std::iota(dimsToExclude.begin(), dimsToExclude.end(), 0); // fill with 0, 1, ... rank-1
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// Nd4jLong numLeft = paddings.e<Nd4jLong>(rank-1,0);
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// Nd4jLong numRight = paddings.e<Nd4jLong>(rank-1,1);
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// Nd4jLong inDimSize = input.sizeAt(rank-1);
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// Nd4jLong outDimSize = output.sizeAt(rank-1);
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// std::vector<std::vector<Nd4jLong>> outIdx = { std::vector<Nd4jLong>(2*rank), {numLeft, numLeft + inDimSize}, {0, numLeft}, {numLeft + inDimSize, outDimSize} };
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// for(int i = 0; i < rank-1; ++i) {
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// outIdx[0][2*i] = paddings.e<Nd4jLong>(i, 0);
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// outIdx[0][2*i + 1] = outIdx[0][2*i] + input.sizeAt(i);
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// }
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// outIdx[0][2*rank-1] = outIdx[0][2*rank-2] = 0;
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// // ***** populate innermost sub-arrays firstly ***** //
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// dimsToExclude.pop_back();
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// Nd4jLong startL = mode == 1 ? 1 : 0; // REFLECT or SYMMETRIC
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// Nd4jLong startR = mode == 1 ? inDimSize-2 : inDimSize-1; // REFLECT or SYMMETRIC
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// Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.shapeInfo(), dimsToExclude);
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// NDArray outSubArr0 = output(outIdx[0], true);
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// PRAGMA_OMP_PARALLEL_FOR
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// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
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// NDArray outSubArr1 = outSubArr0(j, dimsToExclude);
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// NDArray inSubArr = input(j, dimsToExclude);
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// NDArray outSubArrMid = outSubArr1(outIdx[1]);
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// outSubArrMid.assign(inSubArr); // assign middle
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// if(mode == 0) { // CONSTANT
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// if(numLeft != 0) {
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// NDArray temp = outSubArr1(outIdx[2]);
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// temp.assign(padValue); // assign left
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// }
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// if(numRight != 0) {
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// NDArray temp = outSubArr1(outIdx[3]);
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// temp.assign(padValue); // assign right
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// }
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// }
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// else { // REFLECT or SYMMETRIC
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// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) // fill left side
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// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
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// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) // fill right side
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// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
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// }
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// }
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// // ***** fill rest of outer sub-arrays ***** //
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// std::vector<Nd4jLong> outIdxInner(2, 0);
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// std::vector<Nd4jLong> outIdxOuter(2, 0);
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// for(int i = rankBorder - 1; i >= 0; --i) {
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// dimsToExclude.pop_back();
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// outIdxInner.push_back(0), outIdxInner.push_back(0);
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// outIdxOuter.push_back(0), outIdxOuter.push_back(0);
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// Nd4jLong numLeft = paddings.e<Nd4jLong>(i, 0);
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// Nd4jLong numRight = paddings.e<Nd4jLong>(i, 1);
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// if(numLeft == 0 && numRight == 0)
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// continue;
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// Nd4jLong inDimSize = input.sizeAt(i);
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// Nd4jLong outDimSize = output.sizeAt(i);
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// if(mode == 0) {
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// outIdxOuter[0] = 0; outIdxOuter[1] = numLeft;
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// outIdxInner[0] = numLeft + inDimSize; outIdxInner[1] = outDimSize;
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// }
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// startL = mode == 1 ? numLeft + 1 : numLeft; // REFLECT or SYMMETRIC
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// startR = mode == 1 ? numLeft + inDimSize - 2 : numLeft + inDimSize-1; // REFLECT or SYMMETRIC
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// numOfSubArrs = ShapeUtils::getNumOfSubArrs(output.shapeInfo(), dimsToExclude);
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// PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(outIdxOuter, outIdxInner))
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// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
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// NDArray outSubArr = output(j, dimsToExclude);
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// if(mode == 0) { // CONSTANT
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// if(numLeft != 0) {
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// NDArray tempO = outSubArr(outIdxOuter);
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// tempO.assign(padValue); // assign left
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// }
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// if(numRight != 0) {
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// NDArray tempI = outSubArr(outIdxInner);
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// tempI.assign(padValue); // assign right
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// }
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// }
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// else { // REFLECT or SYMMETRIC
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// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) { // fill left side
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// outIdxOuter[0] = k;
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// outIdxOuter[1] = k+1;
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// outIdxInner[0] = e;
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// outIdxInner[1] = e+1;
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// NDArray outSubArrInner = outSubArr(outIdxInner);
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// NDArray outSubArrOuter = outSubArr(outIdxOuter);
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// outSubArrOuter.assign(outSubArrInner);
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// }
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// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) { // fill right side
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// outIdxOuter[0] = k;
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// outIdxOuter[1] = k+1;
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// outIdxInner[0] = e;
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// outIdxInner[1] = e+1;
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// NDArray outSubArrInner = outSubArr(outIdxInner);
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// NDArray outSubArrOuter = outSubArr(outIdxOuter);
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// outSubArrOuter.assign(outSubArrInner);
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// }
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// }
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// }
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// }
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// }
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void pad(sd::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
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BUILD_SINGLE_SELECTOR(input.dataType(), pad_, (mode, input, paddings, output, padValue), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
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// mode: 0 - REFLECT, else - SYMMETRIC
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const int reflBorder = (bool)mode ? 1 : 0;
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const int rank = input.rankOf();
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const Nd4jLong outLen = output.lengthOf();
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if(rank <= 1) {
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const Nd4jLong inLen = input.lengthOf();
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const auto leftSide = paddings.e<Nd4jLong>(0);
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const auto leftSideCorrected = leftSide - reflBorder;
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const Nd4jLong len = 2*(inLen-1) + leftSide + reflBorder;
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for(int i = 0; i < outLen; ++i) {
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if (i < leftSide) // left side
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output.p(i, input.e<T>(leftSideCorrected - i));
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else if(i >= leftSide && i < leftSide + inLen) // middle
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output.p(i, input.e<T>(i - leftSide));
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else // right side
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output.p(i, input.e<T>(len - i));
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}
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}
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else {
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auto func = PRAGMA_THREADS_FOR {
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int inIdx[MAX_RANK], outIdx[MAX_RANK];
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for (auto i = start; i < stop; i++) {
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shape::index2coordsCPU(start, i, output.shapeInfo(), outIdx);
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for (int j = 0; j < rank; ++j) {
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const Nd4jLong inLen = input.sizeAt(j);
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const auto leftSide = paddings.e<T>(j, 0);
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const auto leftSideCorrected = leftSide - reflBorder;
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const Nd4jLong len = 2 * (inLen - 1) + leftSide + reflBorder;
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if (outIdx[j] < leftSide) // left side
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inIdx[j] = leftSideCorrected - outIdx[j];
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else if (outIdx[j] >= leftSide && outIdx[j] < leftSide + inLen) // middle
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inIdx[j] = outIdx[j] - leftSide;
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else // right side
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inIdx[j] = len - outIdx[j];
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}
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auto outOffset = shape::getOffset(output.shapeInfo(), outIdx);
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auto inOffset = shape::getOffset(input.shapeInfo(), inIdx);
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reinterpret_cast<T *>(output.buffer())[outOffset] = reinterpret_cast<T const*>(input.buffer())[inOffset];
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}
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};
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samediff::Threads::parallel_for(func, 0, outLen);
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}
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}
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void mirrorPad(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
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BUILD_SINGLE_SELECTOR(input.dataType(), mirrorPad_, (input, paddings, output, mode), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void mirrorPad_, (const NDArray& input, const NDArray& paddings, NDArray& output, const int mode), LIBND4J_TYPES);
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////////////////////////////////////////////////////////////////////////
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/*// initial values of inIdx, outIdx, dim must be equal to zero
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template<typename T>
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static void recursiveLoopForPad_(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
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int leftOffset;
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// dimensions are array of input dimensions, it is sorted in increasing order
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// every time at the beginning we erase first element from it (not good idea to use vector for this purpose, but luckily it is small enough)
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// then we use this array for tads building, every time while recursion the number of built tads becomes bigger
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dimensions.erase(dimensions.begin());
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// build tad basing on output array, also create auxiliary arrays pointing on required output array ranges
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shape::TAD tadOut(output.shapeInfo(), dimensions.data(), dimensions.size());
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tadOut.createTadOnlyShapeInfo();
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tadOut.createOffsets();
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auto subArrOut = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
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auto subArr = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
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// build tad basing on input array, also create auxiliary array pointing on required input array range
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shape::TAD tadIn(input.shapeInfo(), dimensions.data(), dimensions.size());
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tadIn.createTadOnlyShapeInfo();
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tadIn.createOffsets();
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auto subArrIn = NDArray(input.getBuffer(), tadIn.tadOnlyShapeInfo, output.getContext());
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// these indices take into account recursion and always point to actual tads numbers
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if (input.rankOf() > 1 && output.rankOf() > 1) {// only for non-vector cases
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outIdx = outIdx * output.sizeAt(dim + 1);
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inIdx = inIdx * input.sizeAt(dim + 1);
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}
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// current input tad number, we add to it unity in a loop
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int k = -1;
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// loop through current dimension
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for(int i = 0; i < output.sizeAt(dim); ++i) {
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// corresponds to outer range (relevant indices are absent in input)
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leftOffset = paddings.e<int>(dim, 0);
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if(i < leftOffset || i >= (input.sizeAt(dim) + leftOffset))
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continue;
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// increase input tads number
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++k;
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// recursion condition allows for the fact that tad can't reduce to scalar
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if(dim < input.rankOf() - 2)
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recursiveLoopForPad(mode, input, paddings, output, dimensions, dim + 1, inIdx + k, outIdx + i, padValue);
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else if (paddings.sizeAt(0) > dim + 1){
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leftOffset = paddings.e<int>(dim + 1, 0);
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// shift buffers pointers to actual element position
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if (output.rankOf() > 1) {
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subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + i]);
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subArrIn.setBuffer(reinterpret_cast<T*>(input.getBuffer()) + tadIn.tadOffsets[inIdx + i - paddings.e<int>(dim, 0)]);
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}
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else {
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subArrOut.p(i, subArrIn.e<T>(i - leftOffset));
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}
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// most inner loop, corresponds to last dim = rank-1
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switch (mode) {
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case 0: // CONSTANT mode
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for(int j = 0; j < subArrOut.lengthOf(); ++j)
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if(j < leftOffset || j >= (subArrIn.lengthOf() + leftOffset) ) // firstly fill with zeros outer ranges
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subArrOut.p(j, (T)0.f);
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else
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subArrOut.p(j, subArrIn.e<T>(j - leftOffset)); // fill middle with elements of input array
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break;
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case 1: // REFLECT mode
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for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
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subArrOut.p(leftOffset - j, subArrIn.e<T>(j));
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for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
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subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
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for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
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subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j - 1));
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break;
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case 2: // SYMMETRIC mode
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for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
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subArrOut.p(leftOffset - j, subArrIn.e<T>(j-1));
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for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
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subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
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for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
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subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j));
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break;
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}
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}
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else {
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if (mode == 0 && input.rankOf() < 2)
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subArrOut.p(i, subArrIn.e<T>(i - leftOffset)); // fill middle with elements of input array
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}
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}
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// populate sub-array formed previously
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leftOffset = paddings.e<int>(dim,0);
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switch (mode) {
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case 0: // CONSTANT mode
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for(int j = 1; j <= leftOffset; ++j) {
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// fill left side with padValue
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if (output.rankOf() > 1) {
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subArrOut.setBuffer(
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reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
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subArrOut.assign(padValue);
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}
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else {
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subArrOut.p(j - 1, padValue);
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}
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}
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// output.printIndexedBuffer("Output at");
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for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill left side with zeros
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if (output.rankOf() > 1) {
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subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
|
subArrOut.assign(padValue);
|
|
}
|
|
else {
|
|
subArrOut.p(j, padValue);
|
|
}
|
|
}
|
|
break;
|
|
|
|
case 1: // REFLECT mode
|
|
for(int j = 1; j <= leftOffset; ++j) { // fill left side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - 1 - j]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
break;
|
|
|
|
case 2: // SYMMETRIC mode
|
|
for(int j = 1; j <= leftOffset; ++j) { // fill left side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j - 1]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - j]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
*/
|
|
/*
|
|
void recursiveLoopForPad(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), recursiveLoopForPad_, (mode, input, paddings, output, dimensions, dim, inIdx, outIdx, padValue), LIBND4J_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void recursiveLoopForPad_, (const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue), LIBND4J_TYPES);
|
|
|
|
*/
|
|
|
|
}
|
|
}
|
|
}
|