hedaoyuan
8 years ago
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1b8d2e65ae
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/* 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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#pragma once
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// #include "ConvOp.h"
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namespace paddle {
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/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */
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enum ColFormat { kCFO = 0, kOCF = 1 };
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/*
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* \brief Converts the image data of four dimensions(NCHW) into a colData.
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* Then you can reshape colData to a convolution matrix for
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* convolution calculation based on matrix multiplication.
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*
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* \param imData Image data of NCHW format.
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* The format of imData is:
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* [input_channels, input_height, input_width].
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* \param colData colData data.
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* If the template argument Format is kCFO,
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* the format of colData is:
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* [input_channels,
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* filter_height,
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* filter_width,
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* output_height,
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* output_width]
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* If the template argument Format is kOCF,
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* the format of colData is:
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* [output_height,
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* output_width,
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* input_channels,
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* filter_height,
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* filter_width]
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*/
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template <ColFormat Format, DeviceType Device, class T>
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class Im2ColFunctor {
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public:
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void operator()(const T* imData,
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int inputChannels,
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int inputHeight,
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int inputWidth,
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int filterHeight,
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int filterWidth,
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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 outputHeight,
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int outputWidth,
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T* colData);
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};
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template <ColFormat Format, DeviceType Device, class T>
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class Col2ImFunctor {
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public:
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void operator()(const T* colData,
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int inputChannels,
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int inputHeight,
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int inputWidth,
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int filterHeight,
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int filterWidth,
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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 outputHeight,
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int outputWidth,
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T* imData);
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};
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} // namespace paddle
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@ -0,0 +1,164 @@
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/* 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 "Function.h"
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#include "GemmConvOp.h"
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namespace paddle {
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/*
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* imData = [input_channels, input_height, input_width]
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* colData = [output_height, output_width,
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* input_channels, filter_height, filter_width]
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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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int inputChannels,
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int inputHeight,
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int inputWidth,
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int filterHeight,
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int filterWidth,
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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 outputHeight,
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int outputWidth,
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T* colData) {
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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 =
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outputH * strideHeight + filterH - paddingHeight;
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int imColOffset = outputW * strideWidth + filterW - 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] = T(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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* \brief Converts the image data of four dimensions(NCHW) into
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* a sequence data of three dimensions(NST). Where N is batch size,
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* S is the length of the sequence after each image is expanded,
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* T is the size of each time step in the sequence.
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*
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* \param inputs[0] Image data of NCHW format.
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* \param outputs[0] Sequence data of NST format.
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*/
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template <DeviceType Device>
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class ImageExpandFunction : public FunctionBase {
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public:
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void init(const FuncConfig& config) override {
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// function arguments
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strides_ = config.get<std::vector<size_t>>("strides");
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paddings_ = config.get<std::vector<size_t>>("paddings");
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blocks_ = config.get<std::vector<size_t>>("blocks");
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// number of inputs and outputs
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numInputs_ = 1;
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numOutputs_ = 1;
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}
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void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
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CHECK_EQ(numInputs_, inputs.size());
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CHECK_EQ(numOutputs_, outputs.size());
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const TensorShape& input = inputs[0].shape();
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const TensorShape& output = outputs[0].shape();
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// input argument should be 4-dimensional.
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CHECK_EQ(input.ndims(), (size_t)4);
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// output argument should be 3-dimensional.
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CHECK_EQ(output.ndims(), (size_t)3);
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// The batchSize of the input needs to be equal to
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// the batchSize of the output.
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CHECK_EQ(input[0], output[0]);
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size_t batchSize = input[0];
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size_t inputChannels = input[1];
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size_t inputHeight = input[2];
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size_t inputWidth = input[3];
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size_t seqLength = output[1];
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size_t stepSize = output[2];
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size_t outputHeight =
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1 +
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(inputHeight + 2 * paddingH() - blockH() + strideH() - 1) / strideH();
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size_t outputWidth =
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1 +
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(inputWidth + 2 * paddingW() - blockW() + strideW() - 1) / strideW();
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CHECK_EQ(seqLength, outputHeight * outputWidth);
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CHECK_EQ(stepSize, inputChannels * blockH() * blockH());
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real* inputData = inputs[0].data<real>();
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real* outputData = outputs[0].data<real>();
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Im2ColFunctor<kOCF, Device, real> im2col;
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for (size_t i = 0; i < batchSize; i++) {
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im2col(inputData,
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inputChannels,
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inputHeight,
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inputWidth,
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blockH(),
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blockW(),
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strideH(),
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strideW(),
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paddingH(),
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paddingW(),
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outputHeight,
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outputWidth,
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outputData);
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inputData += inputChannels * inputHeight * inputWidth;
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outputData += seqLength * stepSize;
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}
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}
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protected:
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std::vector<size_t> strides_;
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std::vector<size_t> paddings_;
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std::vector<size_t> blocks_;
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inline int strideH() const { return strides_[0]; }
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inline int strideW() const { return strides_[1]; }
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inline int paddingH() const { return paddings_[0]; }
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inline int paddingW() const { return paddings_[1]; }
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inline int blockH() const { return blocks_[0]; }
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inline int blockW() const { return blocks_[1]; }
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};
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} // namespace paddle
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