97 lines
3.6 KiB
Plaintext
97 lines
3.6 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#include <ops/declarable/helpers/max_pooling.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename Z>
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static _CUDA_G void indicesFiller(void *vz, Nd4jLong *zShapeInfo, Nd4jLong zLength, Nd4jLong part, Nd4jLong bSize) {
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auto z = reinterpret_cast<Z*>(vz);
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for (int b = blockIdx.x; b < bSize; b += gridDim.x) {
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for (Nd4jLong e = threadIdx.x; e < part; e += blockDim.x) {
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z[shape::getIndexOffset(e + b * part, zShapeInfo, zLength)] = static_cast<Z>(e);
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}
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}
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}
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template <typename T, typename Y>
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static void maxPoolingFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector<int> const& params, NDArray* indices) {
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int kY = params[0];
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int kX = params[1];
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int sY = params[2];
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int sX = params[3];
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int pY = params[4];
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int pX = params[5];
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int dY = params[6];
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int dX = params[7];
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int oY = 0;
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int oX = 0;
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const int bSize = input->sizeAt(0);
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const int inD = input->sizeAt(1);
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const int inY = input->sizeAt(2);
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const int inX = input->sizeAt(3);
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const bool isSameMode = params[8] != 0;
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ConvolutionUtils::calcOutSizePool2D(oY, oX, kY, kX, sY, sX, pY, pX, dY, dX, inY, inX, isSameMode);
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if (isSameMode)
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ConvolutionUtils::calcPadding2D(pY, pX, oY, oX, inY, inX, params[0], params[1], params[2], params[3], params[6], params[7]);
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
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ConvolutionUtils::pooling2d(block, *input, *values, kY, kX, sY, sX, pY, pX, dY, dX, PoolingType::MAX_POOL, 1);
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if (nullptr != indices) {
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// for max_pool_with_argmax
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auto total = input->lengthOf();
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auto part = total / bSize;
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indicesFiller<Y><<<256, 256, 1024, *block.launchContext()->getCudaStream()>>>(indices->specialBuffer(), indices->specialShapeInfo(), indices->lengthOf(), part, bSize);
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/*
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for (int k = 0; k < total; )
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for (int i = 0; i < part; i++) {
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indices->p(k++, i);
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}
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*/
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}
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}
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void maxPoolingFunctor(nd4j::LaunchContext * context, nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector<int> const& params, NDArray* indices) {
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NDArray::prepareSpecialUse({values, indices}, {input});
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auto yType = indices == nullptr ? nd4j::DataType::INT64 : indices->dataType();
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BUILD_DOUBLE_SELECTOR(input->dataType(), yType, maxPoolingFunctor_, (block, input, values, params, indices), FLOAT_TYPES, INDEXING_TYPES);
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NDArray::registerSpecialUse({values, indices}, {input});
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}
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}
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}
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} |