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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/03/21 18:13:14 UTC
[GitHub] piiswrong commented on a change in pull request #9688: [MXNET-108] Adding BilinearResize2D and AdaptiveAvgPool2d operators
piiswrong 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_r176187071
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File path: src/operator/bilinear_resize.cc
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@@ -0,0 +1,191 @@
+/*
+ * 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_resize.cc
+ * \brief bilinear resize operator
+ * \author Hang Zhang
+*/
+#include "bilinear_resize-inl.h"
+#include "elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace mshadow;
+
+template<typename xpu, typename DType, typename AccReal>
+void SpatialUpSamplingBilinearUpdateOutput(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);
+ int nbatch = otensor.size(0);
+ int channels = otensor.size(1);
+ int outputHeight = otensor.size(2);
+ int outputWidth = otensor.size(3);
+ int inputHeight = itensor.size(2);
+ int inputWidth = itensor.size(3);
+
+ DType *idata = itensor.dptr_;
+ DType *odata = otensor.dptr_;
+ 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) ? static_cast<float>(inputHeight - 1)/
+ (outputHeight - 1) : 0.f;
+ const float rwidth = (outputWidth > 1) ? static_cast<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) {
+ Tensor<xpu, 4, DType> gradOutput = input[0].get<xpu, 4, DType>(s);
+ Tensor<xpu, 4, DType> gradInput = output[0].get<xpu, 4, DType>(s);
+
+ int nbatch = gradInput.size(0);
+ int channels = gradInput.size(1);
+ int outputHeight = gradOutput.size(2);
+ int outputWidth = gradOutput.size(3);
+ int inputHeight = gradInput.size(2);
+ int inputWidth = gradInput.size(3);
+
+ DType *data1 = gradInput.dptr_;
+ DType *data2 = gradOutput.dptr_;
+ channels = nbatch * channels;
+
+ // special case: same-size matching grids
+ 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;
+ DType* pos1 = &data1[h1 * inputWidth + w1];
+ const DType* pos2 = &data2[h2 * outputWidth + w2];
+ for (int c = 0; c < channels; ++c) {
+ pos1[0] += pos2[0];
+ pos1 += inputWidth * inputHeight;
+ pos2 += outputWidth * outputHeight;
+ }
+ }
+ }
+ return;
+ }
+ const float rheight =(outputHeight > 1) ? static_cast<float>(inputHeight - 1)/
+ (outputHeight - 1) : 0.f;
+ const float rwidth = (outputWidth > 1) ? static_cast<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;
+ DType* pos1 = &data1[h1 * inputWidth + w1];
+ const DType* pos2 = &data2[h2 * outputWidth + w2];
+ for (int c = 0; c < channels; ++c) {
+ pos1[0] += h0lambda * w0lambda * pos2[0];
+ pos1[w1p] += h0lambda * w1lambda * pos2[0];
+ pos1[h1p * inputWidth] += h1lambda * w0lambda * pos2[0];
+ pos1[h1p * inputWidth + w1p] += h1lambda * w1lambda * pos2[0];
+ pos1 += inputWidth * inputHeight;
+ pos2 += outputWidth * outputHeight;
+ }
+ }
+ }
+}
+
+
+DMLC_REGISTER_PARAMETER(BilinearSampleParam);
+
+NNVM_REGISTER_OP(BilinearResize2D)
+.describe(R"code(
+Resize the input, using bilinear interpolation.
Review comment:
Same as AdaptiveAvgPool2D. Add more documentation
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