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deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/helpers/cpu/dilation2d.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
* *****************************************************************************
*/
//
// @autkhor raver119@gmail.com
//
#include <array/DataTypeUtils.h>
#include <execution/Threads.h>
#include <ops/declarable/helpers/dilation2d.h>
#if NOT_EXCLUDED(OP_dilation2d)
namespace sd {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////
template <typename X, typename Z>
static void dilation2d_(NDArray* input, NDArray* weights, NDArray* output, const sd::LongType sH, const sd::LongType sW, const sd::LongType pH,
const sd::LongType pW, const sd::LongType dH, const sd::LongType dW) {
// input [bS, iH, iW, iC]
// weights [kH, kW, iC]
// output [bS, oH, oW, iC]
const X* x = input->bufferAsT<X>();
const X* y = weights->bufferAsT<X>();
Z* z = output->bufferAsT<Z>();
const sd::LongType* xShapeInfo = input->shapeInfo();
const sd::LongType* yShapeInfo = weights->shapeInfo();
const sd::LongType* zShapeInfo = output->shapeInfo();
const sd::LongType bS = input->sizeAt(0);
const sd::LongType iH = input->sizeAt(1);
const sd::LongType iW = input->sizeAt(2);
const sd::LongType iC = input->sizeAt(3);
const sd::LongType kH = weights->sizeAt(0);
const sd::LongType kW = weights->sizeAt(1);
const sd::LongType oH = output->sizeAt(1);
const sd::LongType oW = output->sizeAt(2);
auto func = PRAGMA_THREADS_FOR_2D {
for (auto b = start_x; b < stop_x; b += inc_x) {
for (auto oh = start_y; oh < stop_y; oh += inc_y) {
for (sd::LongType ow = 0; ow < oW; ++ow) {
for (sd::LongType c = 0; c < iC; ++c) {
X max = -DataTypeUtils::max<X>();
for (sd::LongType kh = 0; kh < kH; ++kh) {
const int ih = oh * sH - pH + kh * dH;
if (ih < 0 || ih >= iH) continue;
for (sd::LongType kw = 0; kw < kW; ++kw) {
const int iw = ow * sW - pW + kw * dW;
if (iw < 0 || iw >= iW) continue;
sd::LongType xCoords[4] = {static_cast<sd::LongType>(b), static_cast<sd::LongType>(ih),
static_cast<sd::LongType>(iw), c};
sd::LongType yCoords[3] = {kh, kw, c};
sd::LongType xOffset;
COORDS2INDEX(shape::rank(xShapeInfo), shape::stride(xShapeInfo), xCoords, xOffset);
sd::LongType yOffset;
COORDS2INDEX(shape::rank(yShapeInfo), shape::stride(yShapeInfo), yCoords, yOffset);
const X val = x[xOffset] + y[yOffset];
if (val > max) max = val;
}
}
sd::LongType zCoords[4] = {static_cast<sd::LongType>(b), static_cast<sd::LongType>(oh), ow, c};
sd::LongType zOffset;
COORDS2INDEX(shape::rank(zShapeInfo), shape::stride(zShapeInfo), zCoords, zOffset);
z[zOffset] = static_cast<Z>(max);
}
}
}
}
};
samediff::Threads::parallel_for(func, 0, bS, 1, 0, oH, 1);
}
void dilation2d(sd::LaunchContext* context, NDArray* input, NDArray* weights, NDArray* output, const sd::LongType sH,
const sd::LongType sW, const sd::LongType pH, const sd::LongType pW, const sd::LongType dH, const sd::LongType dW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), dilation2d_, (input, weights, output, sH, sW, pH, pW, dH, dW),
SD_FLOAT_TYPES);
}
} // namespace helpers
} // namespace ops
} // namespace sd
#endif