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

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/* ******************************************************************************
*
*
* 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/random_crop.h>
#include <graph/Context.h>
#if NOT_EXCLUDED(OP_random_shuffle)
namespace sd {
namespace ops {
namespace helpers {
template <typename T>
static sd::Status _randomCropFunctor(graph::Context& context, NDArray* input, NDArray* shape, NDArray* output,
int seed) {
graph::RandomGenerator rngX(context.getRng());
// functions::random::RandomFunction<T>::template execTransform<randomOps::UniformDistribution<T>>(rng,
// output->buffer(), output->shapeInfo(), std::vector<T>({T(0.), shape->e(last)}).data());
// NativeOpExecutioner::execRandom(random::UniformDistribution, rng, output->buffer(), output->shapeInfo(),
// std::vector<T>({T(0.), shape->e<T>(last)}).data());
sd::LongType last = shape->lengthOf() - 1;
rngX.setSeed(seed);
// functions::random::RandomFunction<T>::template execTransform<randomOps::UniformDistribution<T>>(rng,
// output->buffer(), output->shapeInfo(), std::vector<T>({T(0.), shape->getScalar(last)}).data());
for (sd::LongType e = 0; e < output->lengthOf(); ++e) {
T put = rngX.relativeT<T>(e, static_cast<T>(0), static_cast<T>(shape->e<sd::LongType>(last)));
output->p(e, put);
}
sd::LongType maxIndex = output->argMax();
sd::LongType startPos = output->e<sd::LongType>(maxIndex);
sd::LongType lastDim = input->sizeAt(-1);
sd::LongType pos = 0;
sd::LongType width = startPos + shape->e<sd::LongType>(last);
if (width >= lastDim) {
startPos -= (width - lastDim);
width = lastDim;
}
for (sd::LongType i = 0; i < input->lengthOf(); i += lastDim) {
for (sd::LongType k = startPos; k < width && pos < output->lengthOf(); k++) {
output->p(pos++, input->e<T>(i + k));
}
}
return sd::Status::OK;
}
sd::Status randomCropFunctor(graph::Context& context, NDArray* input, NDArray* shape, NDArray* output, int seed) {
BUILD_SINGLE_SELECTOR(input->dataType(), return _randomCropFunctor, (context, input, shape, output, seed),
SD_FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE( sd::Status _randomCropFunctor,
(graph::Context & context, NDArray* input, NDArray* shape, NDArray* output, int seed),
SD_FLOAT_TYPES);
} // namespace helpers
} // namespace ops
} // namespace sd
#endif