99 lines
4.8 KiB
C++
99 lines
4.8 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 sgazeos@gmail.com
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//
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#include <ops/declarable/helpers/axis.h>
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#include <execution/Threads.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static void _extractPatches(NDArray* images, NDArray* output, int sizeRow, int sizeCol, int strideRow, int strideCol, int rateRow, int rateCol, bool theSame){
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std::vector<int> restDims({1, 2, 3}); // the first and the last dims
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ResultSet listOfMatricies = images->allTensorsAlongDimension(restDims);
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ResultSet listOfOutputs = output->allTensorsAlongDimension(restDims);
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// 3D matricies - 2D matricies of vectors (if last dim is greater than 1)
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//int e = 0;
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const int ksizeRowsEffective = sizeRow + (sizeRow - 1) * (rateRow - 1);
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const int ksizeColsEffective = sizeCol + (sizeCol - 1) * (rateCol - 1);
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const int ksize = ksizeRowsEffective * ksizeColsEffective;
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int batchCount = listOfMatricies.size(); //lengthOf() / ksize;
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Nd4jLong lastDim = images->sizeAt(3);
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Nd4jLong outLastDim = output->sizeAt(3);
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Nd4jLong rowDim = images->sizeAt(1);
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Nd4jLong colDim = images->sizeAt(2);
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Nd4jLong outRowDim = output->sizeAt(1);
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Nd4jLong outColDim = output->sizeAt(2);
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auto rowCast = 1; //(sizeRow - 1)*rateRow < outRowDim/sizeRow ?0:1;///(ksize * lastDim > rowDim * ksizeColsEffective + lastDim?1:0);
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auto colCast = 1; //colDim / ksizeColsEffective +2 <= sizeCol?0:1;//(ksize * lastDim > ksizeRowsEffective * colDim + lastDim?1:0);
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if (sizeRow * rateRow < 3)
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rowCast = 0;
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if (sizeCol * rateCol < 3)
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colCast = 0;
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auto func = PRAGMA_THREADS_FOR {
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for (auto batch = 0; batch < stop; batch += increment) {
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auto patch = listOfMatricies.at(batch);
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auto outMatrix = listOfOutputs.at(batch);
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for (Nd4jLong i = 0; i < outRowDim; i++) {
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for (Nd4jLong j = 0; j < outColDim; j++) {
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Nd4jLong pos = 0;
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//for (Nd4jLong k = 0; k < outputLastDim; k++) {
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auto rowStart = i * strideRow - (theSame ? rowCast : 0);
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auto colStart = j * strideCol - (theSame ? colCast : 0);
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auto rowEnd = rowStart + sizeRow * rateRow;
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auto colEnd = colStart + sizeCol * rateCol;
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if (!theSame) {
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rowEnd = math::nd4j_min(rowStart + sizeRow * rateRow, rowDim);
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colEnd = math::nd4j_min(colStart + sizeCol * rateCol, colDim);
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}
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//auto pixel = 0LL;
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for (auto row = rowStart; row < rowEnd; row += rateRow)
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for (auto col = colStart; col < colEnd; col += rateCol)
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for (auto pixel = 0; pixel < lastDim; pixel++) {
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bool setUp = (theSame && row >= 0 && col >= 0 && row < rowDim && col < colDim) ||
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(!theSame);
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if (setUp) {
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outMatrix->t<T>(i, j, pos) = patch->e<T>(row, col, pixel);
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}
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pos++;
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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_tad(func, 0, batchCount);
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}
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void extractPatches(nd4j::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame){
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auto xType = images->dataType();
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BUILD_SINGLE_SELECTOR(xType, _extractPatches, (images, output, sizeRow, sizeCol, stradeRow, stradeCol, rateRow, rateCol, theSame), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void _extractPatches, (NDArray* input, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame), LIBND4J_TYPES);
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}
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}
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} |