cavis/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu

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/*******************************************************************************
* 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 raver119@gmail.com
//
#include <ops/declarable/helpers/max_pooling.h>
#include <ops/declarable/helpers/convolutions.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename Z>
static _CUDA_G void indicesFiller(void *vz, Nd4jLong *zShapeInfo, Nd4jLong zLength, Nd4jLong part, Nd4jLong bSize) {
auto z = reinterpret_cast<Z*>(vz);
for (int b = blockIdx.x; b < bSize; b += gridDim.x) {
for (Nd4jLong e = threadIdx.x; e < part; e += blockDim.x) {
z[shape::getIndexOffset(e + b * part, zShapeInfo, zLength)] = static_cast<Z>(e);
}
}
}
template <typename T, typename Y>
static void maxPoolingFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector<int> const& params, NDArray* indices) {
int kY = params[0];
int kX = params[1];
int sY = params[2];
int sX = params[3];
int pY = params[4];
int pX = params[5];
int dY = params[6];
int dX = params[7];
int oY = 0;
int oX = 0;
const int bSize = input->sizeAt(0);
const int inD = input->sizeAt(1);
const int inY = input->sizeAt(2);
const int inX = input->sizeAt(3);
const bool isSameMode = params[8] != 0;
ConvolutionUtils::calcOutSizePool2D(oY, oX, kY, kX, sY, sX, pY, pX, dY, dX, inY, inX, isSameMode);
if (isSameMode)
ConvolutionUtils::calcPadding2D(pY, pX, oY, oX, inY, inX, params[0], params[1], params[2], params[3], params[6], params[7]);
// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
ConvolutionUtils::pooling2d(block, *input, *values, kY, kX, sY, sX, pY, pX, dY, dX, PoolingType::MAX_POOL, 1);
if (nullptr != indices) {
// for max_pool_with_argmax
auto total = input->lengthOf();
auto part = total / bSize;
indicesFiller<Y><<<256, 256, 1024, *block.launchContext()->getCudaStream()>>>(indices->specialBuffer(), indices->specialShapeInfo(), indices->lengthOf(), part, bSize);
/*
for (int k = 0; k < total; )
for (int i = 0; i < part; i++) {
indices->p(k++, i);
}
*/
}
}
void maxPoolingFunctor(nd4j::LaunchContext * context, nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector<int> const& params, NDArray* indices) {
NDArray::prepareSpecialUse({values, indices}, {input});
auto yType = indices == nullptr ? nd4j::DataType::INT64 : indices->dataType();
BUILD_DOUBLE_SELECTOR(input->dataType(), yType, maxPoolingFunctor_, (block, input, values, params, indices), FLOAT_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({values, indices}, {input});
}
}
}
}