cavis/libnd4j/include/ops/declarable/helpers/cpu/extract_patches.cpp

/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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 sgazeos@gmail.com
//

#include <ops/declarable/helpers/axis.h>
#include <execution/Threads.h>

namespace sd {
namespace ops {
namespace helpers {

    template <typename T>
    static void _extractPatches(NDArray* images, NDArray* output, int sizeRow, int sizeCol, int strideRow, int strideCol, int rateRow, int rateCol, bool theSame){
        std::vector<int> restDims({1, 2, 3}); // the first and the last dims
        ResultSet listOfMatricies = images->allTensorsAlongDimension(restDims);
        ResultSet listOfOutputs = output->allTensorsAlongDimension(restDims);
        // 3D matricies - 2D matricies of vectors (if last dim is greater than 1)
        //int e = 0;
        const int ksizeRowsEffective = sizeRow + (sizeRow - 1) * (rateRow - 1);
        const int ksizeColsEffective = sizeCol + (sizeCol - 1) * (rateCol - 1);
        const int ksize = ksizeRowsEffective * ksizeColsEffective;
        int batchCount = listOfMatricies.size(); //lengthOf() / ksize;
        Nd4jLong lastDim = images->sizeAt(3);
        Nd4jLong outLastDim = output->sizeAt(3);
        Nd4jLong rowDim = images->sizeAt(1);
        Nd4jLong colDim = images->sizeAt(2);
        Nd4jLong outRowDim = output->sizeAt(1);
        Nd4jLong outColDim = output->sizeAt(2);
        auto rowCast = 1; //(sizeRow - 1)*rateRow < outRowDim/sizeRow  ?0:1;///(ksize * lastDim > rowDim * ksizeColsEffective + lastDim?1:0);
        auto colCast = 1; //colDim / ksizeColsEffective +2 <= sizeCol?0:1;//(ksize * lastDim > ksizeRowsEffective * colDim + lastDim?1:0);
        if (sizeRow * rateRow < 3)
            rowCast = 0;
        if (sizeCol * rateCol < 3)
            colCast = 0;

       auto func = PRAGMA_THREADS_FOR {
           for (auto batch = 0; batch < stop; batch++) {
               auto patch = listOfMatricies.at(batch);
               auto outMatrix = listOfOutputs.at(batch);

               for (Nd4jLong i = 0; i < outRowDim; i++) {
                   for (Nd4jLong j = 0; j < outColDim; j++) {
                       Nd4jLong pos = 0;
                       //for (Nd4jLong k = 0; k < outputLastDim; k++) {
                       auto rowStart = i * strideRow - (theSame ? rowCast : 0);
                       auto colStart = j * strideCol - (theSame ? colCast : 0);
                       auto rowEnd = rowStart + sizeRow * rateRow;
                       auto colEnd = colStart + sizeCol * rateCol;
                       if (!theSame) {
                           rowEnd = math::nd4j_min(rowStart + sizeRow * rateRow, rowDim);
                           colEnd = math::nd4j_min(colStart + sizeCol * rateCol, colDim);
                       }
                       //auto pixel = 0LL;
                       for (auto row = rowStart; row < rowEnd; row += rateRow)
                           for (auto col = colStart; col < colEnd; col += rateCol)
                               for (auto pixel = 0; pixel < lastDim; pixel++) {
                                   bool setUp = (theSame && row >= 0 && col >= 0 && row < rowDim && col < colDim) ||
                                                (!theSame);
                                   if (setUp) {
                                       outMatrix->r<T>(i, j, pos) = patch->e<T>(row, col, pixel);
                                   }
                                   pos++;
                               }
                   }
               }
           }
       };

       samediff::Threads::parallel_tad(func, 0, batchCount);
    }


    void extractPatches(sd::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame){
        auto xType = images->dataType();

        BUILD_SINGLE_SELECTOR(xType, _extractPatches, (images, output, sizeRow, sizeCol, stradeRow, stradeCol, rateRow, rateCol, theSame), LIBND4J_TYPES);
    }

    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);

}
}
}