Files
deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/helpers/cpu/axis.cpp
T
2026-07-13 12:47:05 +08:00

58 lines
1.8 KiB
C++

/* ******************************************************************************
*
*
* 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.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* 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 sd {
namespace ops {
namespace helpers {
void adjustAxis(sd::LongType rank, NDArray* axisVector, std::vector<LongType>& output) {
if(axisVector->isScalar()) {
output.resize(1);
auto ca = axisVector->e<sd::LongType>(0);
if (ca < 0) // shift values on rank for negative vals
ca += rank;
output[0] = ca;
return;
}
output.resize(axisVector->lengthOf());
axisVector->tickReadDevice(); // mark input as read on device
axisVector->syncToHost(); // sync to host
for (int e = 0; e < axisVector->lengthOf(); e++) {
auto ca = axisVector->e<sd::LongType>(e);
if (ca < 0) // shift values on rank for negative vals
ca += rank;
output[e] = ca;
}
}
void adjustAxis(sd::LongType rank, std::vector<LongType>& axisVector) {
for (size_t e = 0; e < axisVector.size(); e++) {
auto a = axisVector[e];
if (a < 0) axisVector[e] = a + rank;
}
}
} // namespace helpers
} // namespace ops
} // namespace sd