zhanghang1989 commented on a change in pull request #9688: [MXNET-108] Adding BilinearResize2D and AdaptiveAvgPool2d operators URL: https://github.com/apache/incubator-mxnet/pull/9688#discussion_r179983326
########## File path: src/operator/contrib/adaptive_avg_pooling.cc ########## @@ -0,0 +1,232 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://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. + */ +/*! + * Copyright (c) 2018 by Contributors + * \file adaptive_avg_pooling.cc + * \brief adaptive average pooling operator + * \author Hang Zhang +*/ +#include "adaptive_avg_pooling-inl.h" +// #include "elemwise_op_common.h" +#include "../elemwise_op_common.h" + +#define START_IND(a, b, c) static_cast<int>(floor(static_cast<float>(a * c) / b)) +#define END_IND(a, b, c) static_cast<int>(ceil(static_cast<float>((a + 1) * c) / b)) + +namespace mxnet { +namespace op { + +using namespace mshadow; + +template<typename real> +static void SpatialAdaptiveAveragePooling_updateOutput_frame( + real *input_p, + real *output_p, + int64_t sizeD, + int64_t isizeH, + int64_t isizeW, + int64_t osizeH, + int64_t osizeW, + int64_t istrideD, + int64_t istrideH, + int64_t istrideW) { + int64_t d; +#pragma omp parallel for private(d) \ +num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (d = 0; d < sizeD; d++) { + /* loop over output */ + int64_t oh, ow, ih, iw; + int outOffset = d*osizeH*osizeW; + for (oh = 0; oh < osizeH; oh++) { + int istartH = START_IND(oh, osizeH, isizeH); + int startOffsetH = istartH * istrideH; + int outOffsetH = oh * osizeW; + int iendH = END_IND(oh, osizeH, isizeH); + int kH = iendH - istartH; + + for (ow = 0; ow < osizeW; ow++) { + int istartW = START_IND(ow, osizeW, isizeW); + int iendW = END_IND(ow, osizeW, isizeW); + int kW = iendW - istartW; + + /* local pointers */ + real *ip = input_p + d*istrideD + startOffsetH + istartW*istrideW; + real *op = output_p + outOffset + outOffsetH + ow; + + /* compute local average: */ + real sum = 0; + for (ih = 0; ih < kH; ih++) { + int ihOffset = ih*istrideH; + for (iw = 0; iw < kW; iw++) { + real val = *(ip + ihOffset + iw*istrideW); + sum += val; + } + } + + /* set output to local average */ + *op = sum / kW / kH; + } + } + } +} + +template<typename real> +static void SpatialAdaptiveAveragePooling_updateGradInput_frame( + real *gradInput_p, + real *gradOutput_p, + int64_t sizeD, + int64_t isizeH, + int64_t isizeW, + int64_t osizeH, + int64_t osizeW) { + int64_t d; +#pragma omp parallel for private(d) \ +num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (d = 0; d < sizeD; d++) { + real *gradInput_p_d = gradInput_p + d*isizeW*isizeH; + real *gradOutput_p_d = gradOutput_p + d*osizeW*osizeH; + + /* calculate average */ + int64_t oh, ow; + for (oh = 0; oh < osizeH; oh++) { + int istartH = START_IND(oh, osizeH, isizeH); + int iendH = END_IND(oh, osizeH, isizeH); + int kH = iendH - istartH; + + for (ow = 0; ow < osizeW; ow++) { + int istartW = START_IND(ow, osizeW, isizeW); + int iendW = END_IND(ow, osizeW, isizeW); + int kW = iendW - istartW; + + real grad_delta = gradOutput_p_d[oh*osizeW +ow] / kH / kW; + + int ih, iw; + for (ih = istartH; ih < iendH; ih++) { + for (iw = istartW; iw < iendW; iw++) { + /* update gradient */ + gradInput_p_d[ih*isizeW + iw] += grad_delta; + } + } + } + } + } +} + + +template<typename xpu, typename DType, typename AccReal> +void AdaptiveAvgPoolUpdateOutput(mshadow::Stream<cpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output) { + Tensor<xpu, 4, DType> itensor = input[0].get<xpu, 4, DType>(s); + Tensor<xpu, 4, DType> otensor = output[0].get<xpu, 4, DType>(s); + + DType *input_data = itensor.dptr_; + DType *output_data = otensor.dptr_; + + int64_t sizeB = itensor.size(0); + int64_t sizeD = itensor.size(1); + int64_t isizeH = itensor.size(2); + int64_t isizeW = itensor.size(3); + + int64_t istrideB = get_stride<xpu, 4, DType>(itensor, 0); + int64_t istrideD = get_stride<xpu, 4, DType>(itensor, 1); + int64_t istrideH = get_stride<xpu, 4, DType>(itensor, 2); + int64_t istrideW = get_stride<xpu, 4, DType>(itensor, 3); + + int64_t osizeH = otensor.size(2); + int64_t osizeW = otensor.size(3); + + int64_t b; +#pragma omp parallel for private(b) \ +num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (b = 0; b < sizeB; b++) { + SpatialAdaptiveAveragePooling_updateOutput_frame<DType>( + input_data+b*istrideB, output_data+b*sizeD*osizeH*osizeW, + sizeD, + isizeH, isizeW, + osizeH, osizeW, + istrideD, + istrideH, istrideW); + } +} + + +template<typename xpu, typename DType, typename AccReal> +void AdaptiveAvgPoolUpdateGradInput(mshadow::Stream<cpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output) { + Tensor<xpu, 4, DType> gradOut = input[0].get<xpu, 4, DType>(s); + Tensor<xpu, 4, DType> gradIn = output[0].get<xpu, 4, DType>(s); + + DType *gradOutput_data = gradOut.dptr_; + DType *gradInput_data = gradIn.dptr_; + + int64_t sizeB = gradIn.size(0); + int64_t sizeD = gradIn.size(1); + int64_t isizeH = gradIn.size(2); + int64_t isizeW = gradIn.size(3); + + int64_t osizeH = gradOut.size(2); + int64_t osizeW = gradOut.size(3); + + int64_t b; +#pragma omp parallel for private(b) \ +num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (b = 0; b < sizeB; b++) { + SpatialAdaptiveAveragePooling_updateGradInput_frame<DType>( + gradInput_data+b*sizeD*isizeH*isizeW, gradOutput_data+b*sizeD*osizeH*osizeW, + sizeD, + isizeH, isizeW, + osizeH, osizeW); + } +} + + +DMLC_REGISTER_PARAMETER(AdaptiveAvgPoolParam); + +NNVM_REGISTER_OP(_contrib_AdaptiveAvgPooling2D) +.describe(R"code( +Applies a 2D adaptive average pooling over an input signal composed of several input planes. + + The output size is (N x C x output_size x output_size), for any input (NCHW). 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