88 lines
3.2 KiB
C++
88 lines
3.2 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 Yurii Shyrma (iuriish@yahoo.com), created on 07.03.2019
|
|
//
|
|
|
|
#include <ops/declarable/helpers/gather.h>
|
|
#include <numeric>
|
|
|
|
namespace nd4j {
|
|
namespace ops {
|
|
namespace helpers {
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
|
|
|
|
int axis = intArgs.size() > 0 ? intArgs[0] : 0;
|
|
const int inputRank = input->rankOf();
|
|
if(axis < 0)
|
|
axis += inputRank;
|
|
|
|
const int numOfIntArgs = intArgs.size();
|
|
|
|
if (indices != nullptr) {
|
|
|
|
// first case: indices consist of only one scalar
|
|
if(indices->isScalar()) {
|
|
|
|
if(input->rankOf() <= 1){
|
|
//For scalar indices, rank 0 or 1 input: can't do tensor along dimension 0 as this is whole array... instead, we want to get a scalar
|
|
auto idx = indices->e<Nd4jLong>(0);
|
|
auto scalarNDArray = input->e(idx);
|
|
output->assign(scalarNDArray);
|
|
}
|
|
else {
|
|
NDArray inSubArr = (*input)(indices->e<Nd4jLong>(0), {axis});
|
|
output->assign(inSubArr);
|
|
}
|
|
}
|
|
else {
|
|
|
|
std::vector<int> dimsOut(indices->rankOf());
|
|
std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... axis+indices->rankOf()-1
|
|
const Nd4jLong numOfSubArrs = indices->lengthOf();
|
|
#pragma omp parallel for if(numOfSubArrs > Environment::getInstance()->elementwiseThreshold()) schedule(guided)
|
|
for(int i = 0; i < numOfSubArrs; ++i) {
|
|
NDArray subArrOut = (*output)(i, dimsOut);
|
|
NDArray subArrIn = (*input)(indices->e<Nd4jLong>(i), {axis});
|
|
subArrOut.assign(subArrIn);
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
|
|
// we only allow scalar/vector case here
|
|
if (numOfIntArgs == 2) { // scalar case
|
|
output->assign((*input)(intArgs[1], {axis}));
|
|
}
|
|
else { // vector case
|
|
const Nd4jLong numOfSubArrs = intArgs.size() - 1;
|
|
#pragma omp parallel for if(numOfSubArrs > Environment::getInstance()->elementwiseThreshold()) schedule(guided)
|
|
for(int i = 0; i < numOfSubArrs; ++i) {
|
|
NDArray subArrOut = (*output)(i, {axis});
|
|
NDArray subArrIn = (*input)(intArgs[i+1], {axis});
|
|
subArrOut.assign(subArrIn);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
}
|
|
}
|
|
} |