/*******************************************************************************
* 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>
namespace nd4j {
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
std::unique_ptr<ResultSet> listOfMatricies(images->allTensorsAlongDimension(restDims));
std::unique_ptr<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;
//Nd4jLong outputLastDim = output->sizeAt(3);
PRAGMA_OMP_PARALLEL_FOR
for (Nd4jLong batch = 0; batch < batchCount; batch++) {
auto patch = listOfMatricies->at(batch);
auto outMatrix = listOfOutputs->at(batch);
//auto patchBorder = patch->sizeAt(0);
if (theSame) { // SAME case
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 - rowCast;
auto colStart = j * strideCol - colCast;
auto rowEnd = rowStart + sizeRow * rateRow;
auto colEnd = colStart + sizeCol * rateCol;
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++) {
if (row >=0 && col >= 0 && row < rowDim && col < colDim)
outMatrix->p<T>(i, j, pos, patch->e<T>(row, col, pixel));
pos++;
}
//}
}
}
} else { // VALID case
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;
auto colStart = j * strideCol;
auto rowEnd = math::nd4j_min(rowStart + sizeRow * rateRow, rowDim);
auto 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++)
outMatrix->p<T>(i,j,pos++, patch->e<T>(row, col, pixel));
//}
}
}
}
}
////#pragma omp parallel for
// for (Nd4jLong e = 0; e < batchCount; ++e) {
// auto patch = listOfMatricies->at(e);
// auto outMatrix = listOfOutputs->at(e);
// auto patchBorder = patch->sizeAt(0);
// //int startRow = 0;
// //int startCol = 0;
// Nd4jLong pos = 0;
// for (int i = 0; i < rowDim; i += stradeRow)
// for (int j = 0; j < colDim; j += stradeCol)
// for (int l = 0; l < ksizeRowsEffective; l++)
// for (int m = 0; m < ksizeColsEffective; m++) {
// //for (Nd4jLong pos = 0; pos < outputLastDim; pos++)
// for (Nd4jLong k = 0; k < lastDim; ++k) {
// if (theSame) {
// if (j + m * rateCol < colDim &&
// i + l * rateRow < rowDim)
// outMatrix->p<T>(i, j, pos++, patch->e<T>(i + rateRow * l, j + m * rateCol, k));
//// pos ++; //= ksize;
// if (pos >= outLastDim) {
// pos = 0;
// //break;
// }
// }
// else {
//// if (l + i < rowDim && m + j < colDim && i + rateRow * l < patchBorder) // && i + rateRow * l < sizeRow && j + m * rateCol < sizeCol
//// outMatrix->p<T>(i, j, pos, patch->e<T>(i + rateRow * l, j + m * rateCol, k));
// if (j + m * rateCol < colDim &&
// i + l * rateRow < rowDim) // && i + rateRow * l < sizeRow && j + m * rateCol < sizeCol
// outMatrix->p<T>(pos++, patch->e<T>(i + rateRow * l, j + m * rateCol, k));
// //pos++;
//// if (pos >= outLastDim)
//// pos = 0;
// if (pos >= outMatrix->lengthOf()) { // stop looping and try next batch
// k = lastDim;
// m = sizeCol;
// l = sizeRow;
// j = colDim;
// i = rowDim;
// }
// }
// }
// }
// }
}
void extractPatches(nd4j::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);
}
}
}