piiswrong commented on a change in pull request #9688: bilinear upsample from PyTorch URL: https://github.com/apache/incubator-mxnet/pull/9688#discussion_r165805994
########## File path: src/operator/bilinear_upsample.cc ########## @@ -0,0 +1,185 @@ +/* + * 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 bilinear_upsample.cc + * \brief bilinear upsample operator + * \author Hang Zhang + * Adapted from PyTorch +*/ +#include "devicetensor.h" +#include "bilinear_upsample-inl.h" +#include "elemwise_op_common.h" + +namespace mxnet { +namespace op { + + +template<typename xpu, typename DType, typename AccReal> +void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<cpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output) { + DeviceTensor<DType, 4> itensor = devicetensor<DType, 4>(input[0]); + DeviceTensor<DType, 4> otensor = devicetensor<DType, 4>(output[0]); + int nbatch = otensor.getSize(0); + int channels = otensor.getSize(1); + int outputHeight = otensor.getSize(2); + int outputWidth = otensor.getSize(3); + int inputHeight = itensor.getSize(2); + int inputWidth = itensor.getSize(3); + + DType *idata = itensor.data_ptr(); + DType *odata = otensor.data_ptr(); + channels = nbatch * channels; + // special case: just copy + if (inputHeight == outputHeight && inputWidth == outputWidth) { + for (int h2 = 0; h2 < outputHeight; ++h2) { + const int h1 = h2; + for (int w2 = 0; w2 < outputWidth; ++w2) { + const int w1 = w2; + const DType* pos1 = &idata[h1 * inputWidth + w1]; + DType* pos2 = &odata[h2 * outputWidth + w2]; + for (int c = 0; c < channels; ++c) { + pos2[0] = pos1[0]; + pos1 += inputWidth * inputHeight; + pos2 += outputWidth * outputHeight; + } + } + } + return; + } + const float rheight =(outputHeight > 1) ? (float)(inputHeight - 1)/(outputHeight - 1) : 0.f; + const float rwidth = (outputWidth > 1) ? (float)(inputWidth - 1) / (outputWidth - 1) : 0.f; + for (int h2 = 0; h2 < outputHeight; ++h2) { + const float h1r = rheight * h2; + const int h1 = h1r; + const int h1p = (h1 < inputHeight - 1) ? 1 : 0; + const DType h1lambda = h1r - h1; + const DType h0lambda = (DType)1. - h1lambda; + for (int w2 = 0; w2 < outputWidth; ++w2) { + const float w1r = rwidth * w2; + const int w1 = w1r; + const int w1p = (w1 < inputWidth - 1) ? 1 : 0; + const DType w1lambda = w1r - w1; + const DType w0lambda = (DType)1. - w1lambda; + const DType* pos1 = &idata[h1 * inputWidth + w1]; + DType* pos2 = &odata[h2 * outputWidth + w2]; + for (int c = 0; c < channels; ++c) { + pos2[0] = h0lambda * (w0lambda * pos1[0]+ w1lambda * pos1[w1p]) + + h1lambda * (w0lambda * pos1[h1p * inputWidth] + + w1lambda * pos1[h1p * inputWidth + w1p]); + pos1 += inputWidth * inputHeight; + pos2 += outputWidth * outputHeight; + } + } + } +} + + +template<typename xpu, typename DType, typename AccReal> +void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output) { + DeviceTensor<DType, 4> gradOutput = devicetensor<DType, 4>(input[0]); + DeviceTensor<DType, 4> gradInput = devicetensor<DType, 4>(output[0]); + int nbatch = gradInput.getSize(0); + int channels = gradInput.getSize(1); + int outputHeight = gradInput.getSize(2); + int outputWidth = gradInput.getSize(3); + int inputHeight = gradOutput.getSize(2); + int inputWidth = gradOutput.getSize(3); + + DType *data1 = gradInput.data_ptr(); + DType *data2 = gradOutput.data_ptr(); + channels = nbatch * channels; + + // special case: same-size matching grids Review comment: This should be handled at the top level with a identity compute function ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
