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246 lines
9.0 KiB
246 lines
9.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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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,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "Im2Col.h"
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namespace paddle {
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/*
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* imShape = [inputChannels, inputHeight, inputWidth]
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* colShape =
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* [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth]
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*/
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template <class T>
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class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, T> {
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public:
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void operator()(const T* imData,
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const TensorShape& imShape,
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T* colData,
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const TensorShape& colShape,
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int strideHeight,
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int strideWidth,
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int paddingHeight,
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int paddingWidth,
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int dilationHeight,
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int dilationWidth) {
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int inputChannels = imShape[0];
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int inputHeight = imShape[1];
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int inputWidth = imShape[2];
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int filterHeight = colShape[1];
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int filterWidth = colShape[2];
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int outputHeight = colShape[3];
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int outputWidth = colShape[4];
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int channelsCol = inputChannels * filterHeight * filterWidth;
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for (int c = 0; c < channelsCol; ++c) {
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int wOffset = c % filterWidth;
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int hOffset = (c / filterWidth) % filterHeight;
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int c_im = c / filterWidth / filterHeight;
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for (int h = 0; h < outputHeight; ++h) {
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for (int w = 0; w < outputWidth; ++w) {
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int imRowIdx = h * strideHeight + hOffset * dilationHeight;
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int imColIdx = w * strideWidth + wOffset * dilationWidth;
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if ((imRowIdx - paddingHeight) < 0 ||
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(imRowIdx - paddingHeight) >= inputHeight ||
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(imColIdx - paddingWidth) < 0 ||
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(imColIdx - paddingWidth) >= inputWidth) {
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colData[(c * outputHeight + h) * outputWidth + w] = T(0);
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} else {
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imRowIdx += c_im * inputHeight - paddingHeight;
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imColIdx -= paddingWidth;
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colData[(c * outputHeight + h) * outputWidth + w] =
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imData[imRowIdx * inputWidth + imColIdx];
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}
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}
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}
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}
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}
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};
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/*
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* imShape = [inputChannels, inputHeight, inputWidth]
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* colShape =
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* [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth]
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*/
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template <class T>
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class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, T> {
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public:
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void operator()(T* imData,
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const TensorShape& imShape,
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const T* colData,
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const TensorShape& colShape,
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int strideHeight,
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int strideWidth,
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int paddingHeight,
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int paddingWidth,
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int dilationHeight,
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int dilationWidth) {
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int inputChannels = imShape[0];
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int inputHeight = imShape[1];
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int inputWidth = imShape[2];
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int filterHeight = colShape[1];
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int filterWidth = colShape[2];
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int outputHeight = colShape[3];
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int outputWidth = colShape[4];
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int channelsCol = inputChannels * filterHeight * filterWidth;
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for (int c = 0; c < channelsCol; ++c) {
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int wOffset = c % filterWidth;
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int hOffset = (c / filterWidth) % filterHeight;
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int c_im = c / filterWidth / filterHeight;
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for (int h = 0; h < outputHeight; ++h) {
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for (int w = 0; w < outputWidth; ++w) {
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int imRowIdx = h * strideHeight + hOffset * dilationHeight;
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int imColIdx = w * strideWidth + wOffset * dilationWidth;
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if ((imRowIdx - paddingHeight) >= 0 &&
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(imRowIdx - paddingHeight) < inputHeight &&
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(imColIdx - paddingWidth) >= 0 &&
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(imColIdx - paddingWidth) < inputWidth) {
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imRowIdx += c_im * inputHeight - paddingHeight;
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imColIdx -= paddingWidth;
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imData[imRowIdx * inputWidth + imColIdx] +=
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colData[(c * outputHeight + h) * outputWidth + w];
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}
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}
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}
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}
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}
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};
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template class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, float>;
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template class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, double>;
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template class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, float>;
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template class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, double>;
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/*
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* imShape = [inputChannels, inputHeight, inputWidth]
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* colShape =
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* [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
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*/
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template <class T>
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class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, T> {
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public:
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void operator()(const T* imData,
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const TensorShape& imShape,
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T* colData,
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const TensorShape& colShape,
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int strideHeight,
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int strideWidth,
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int paddingHeight,
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int paddingWidth,
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int dilationHeight = 1,
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int dilationWidth = 1) {
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int inputChannels = imShape[0];
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int inputHeight = imShape[1];
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int inputWidth = imShape[2];
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int filterHeight = colShape[3];
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int filterWidth = colShape[4];
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int outputHeight = colShape[0];
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int outputWidth = colShape[1];
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for (int outputH = 0; outputH < outputHeight; ++outputH) {
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for (int outputW = 0; outputW < outputWidth; ++outputW) {
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for (int channel = 0; channel < inputChannels; ++channel) {
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for (int filterH = 0; filterH < filterHeight; ++filterH) {
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for (int filterW = 0; filterW < filterWidth; ++filterW) {
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int imRowOffset = outputH * strideHeight +
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filterH * dilationHeight - paddingHeight;
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int imColOffset = outputW * strideWidth +
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filterW * dilationWidth - paddingWidth;
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int colDataOffset =
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(((outputH * outputWidth + outputW) * inputChannels +
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channel) *
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filterHeight +
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filterH) *
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filterWidth +
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filterW;
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if (imRowOffset < 0 || imRowOffset >= inputHeight ||
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imColOffset < 0 || imColOffset >= inputWidth) {
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colData[colDataOffset] = float(0);
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} else {
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int imDataOffset =
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(channel * inputHeight + imRowOffset) * inputWidth +
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imColOffset;
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colData[colDataOffset] = imData[imDataOffset];
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}
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}
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}
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}
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}
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}
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}
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};
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/*
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* imShape = [inputChannels, inputHeight, inputWidth]
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* colShape =
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* [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
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*/
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template <class T>
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class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, T> {
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public:
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void operator()(T* imData,
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const TensorShape& imShape,
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const T* colData,
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const TensorShape& colShape,
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int strideHeight,
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int strideWidth,
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int paddingHeight,
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int paddingWidth,
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int dilationHeight = 1,
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int dilationWidth = 1) {
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int inputChannels = imShape[0];
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int inputHeight = imShape[1];
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int inputWidth = imShape[2];
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int filterHeight = colShape[3];
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int filterWidth = colShape[4];
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int outputHeight = colShape[0];
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int outputWidth = colShape[1];
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for (int outputH = 0; outputH < outputHeight; ++outputH) {
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for (int outputW = 0; outputW < outputWidth; ++outputW) {
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for (int channel = 0; channel < inputChannels; ++channel) {
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for (int filterH = 0; filterH < filterHeight; ++filterH) {
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for (int filterW = 0; filterW < filterWidth; ++filterW) {
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int imRowOffset = outputH * strideHeight +
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filterH * dilationHeight - paddingHeight;
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int imColOffset = outputW * strideWidth +
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filterW * dilationWidth - paddingWidth;
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int colDataOffset =
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(((outputH * outputWidth + outputW) * inputChannels +
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channel) *
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filterHeight +
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filterH) *
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filterWidth +
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filterW;
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if (imRowOffset >= 0 && imRowOffset < inputHeight &&
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imColOffset >= 0 && imColOffset < inputWidth) {
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int imDataOffset =
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(channel * inputHeight + imRowOffset) * inputWidth +
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imColOffset;
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imData[imDataOffset] += colData[colDataOffset];
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}
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}
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}
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}
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}
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}
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}
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};
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template class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, float>;
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template class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, double>;
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template class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, float>;
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template class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, double>;
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} // namespace paddle
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