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

99 lines
4.8 KiB
C++

/*******************************************************************************
* 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 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
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 += increment) {
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->t<T>(i, j, pos) = patch->e<T>(row, col, pixel);
}
pos++;
}
}
}
}
};
samediff::Threads::parallel_tad(func, 0, batchCount);
}
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);
}
}
}