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Use the TensorShape to reconstruct the arguments of the Im2ColFunctor and Col2ImFunctor interfaces.

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cblas_new
hedaoyuan 8 years ago
parent 2acb84fe70
commit 0672d330a3
2 changed files with 145 additions and 67 deletions
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92
paddle/function/Im2Col.h
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@ -0,0 +1,92 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed 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. */
#pragma once
namespace paddle {
/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */
enum ColFormat { kCFO = 0, kOCF = 1 };
/*
* \brief Converts the image data of three dimensions(CHW) into a colData of
* five dimensions in the Im2ColFunctor calculation,
* And in the Col2ImFunctor calculation, it is reversed.
*
* \param imData Image data of NCHW format.
* The shape of imData is:
* [inputChannels, inputHeight, inputWidth].
* \param colData colData data.
*
* If the template argument Format is kCFO, the shape of colData is:
* [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth]
* So, it is easy to reshape into a convolution matrix for convolution
* calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the height is equal
* inputChannels * filterHeight * filterWidth, and the width is equal
* outputHeight * outputWidth.
*
* Reshape:
* shape of colData shape of sequence
* [inputChannels,
* filterHeight,
* filterWidth, ======> [seqLength, stepSize]
* outputHeight,
* outputWidth]
*
* If the template argument Format is kOCF, the shape of colData is:
* [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
* So, it is easy to reshape into a sequence matrix for rnn calculation.
* The shape of sequence matrix is [seqLength, stepSize], where the seqLength
* is equal outputHeight * outputWidth, and the stepSize is equal
* inputChannels * filterHeight * filterWidth.
*
* Reshape:
* shape of colData shape of sequence
* [outputHeight,
* outputWidth,
* inputChannels, ======> [seqLength, stepSize]
* filterHeight,
* filterWidth]
*
* \note The caller needs to ensure that imShape.inputChannels is equal to
* colShape.inputChannels.
*/
template <ColFormat Format, DeviceType Device, class T>
class Im2ColFunctor {
public:
void operator()(const T* imData,
const TensorShape& imShape,
T* colData,
const TensorShape& colShape,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth);
};
template <ColFormat Format, DeviceType Device, class T>
class Col2ImFunctor {
public:
void operator()(T* imData,
const TensorShape& imShape,
const T* colData,
const TensorShape& colShape,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth);
};
} // namespace paddle

120
paddle/function/ImageExpandOp.cpp
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@ -13,31 +13,33 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "Function.h"
#include "GemmConvOp.h"
#include "Im2Col.h"
namespace paddle {
/*
* imData = [input_channels, input_height, input_width]
* colData = [output_height, output_width,
* input_channels, filter_height, filter_width]
* imShape = [inputChannels, inputHeight, inputWidth]
* colShape =
* [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
*/
template <class T>
class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, T> {
public:
void operator()(const T* imData,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
int filterWidth,
const TensorShape& imShape,
T* colData,
const TensorShape& colShape,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth,
int outputHeight,
int outputWidth,
T* colData) {
int paddingWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
int filterHeight = colShape[3];
int filterWidth = colShape[4];
int outputHeight = colShape[0];
int outputWidth = colShape[1];
for (int outputH = 0; outputH < outputHeight; ++outputH) {
for (int outputW = 0; outputW < outputWidth; ++outputW) {
for (int channel = 0; channel < inputChannels; ++channel) {
@ -55,7 +57,7 @@ public:
filterW;
if (imRowOffset < 0 || imRowOffset >= inputHeight ||
imColOffset < 0 || imColOffset >= inputWidth) {
colData[colDataOffset] = T(0);
colData[colDataOffset] = float(0);
} else {
int imDataOffset =
(channel * inputHeight + imRowOffset) * inputWidth +
@ -70,22 +72,29 @@ public:
}
};
/*
* imShape = [inputChannels, inputHeight, inputWidth]
* colShape =
* [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
*/
template <class T>
class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, T> {
public:
void operator()(const T* colData,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
int filterWidth,
void operator()(T* imData,
const TensorShape& imShape,
const T* colData,
const TensorShape& colShape,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth,
int outputHeight,
int outputWidth,
T* imData) {
int paddingWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
int filterHeight = colShape[3];
int filterWidth = colShape[4];
int outputHeight = colShape[0];
int outputWidth = colShape[1];
for (int outputH = 0; outputH < outputHeight; ++outputH) {
for (int outputW = 0; outputW < outputWidth; ++outputW) {
for (int channel = 0; channel < inputChannels; ++channel) {
@ -146,7 +155,7 @@ public:
virtual void calc(const BufferArgs& inputs, const BufferArgs& outputs) {}
void check(const TensorShape& image, const TensorShape& sequence) {
void check(const TensorShape& image, const TensorShape& sequence) const {
// image shape should be 4-dimensional.
CHECK_EQ(image.ndims(), (size_t)4);
// sequence shape should be 3-dimensional.
@ -159,7 +168,7 @@ public:
// Calculate the shape of colData based on the shape of the image
// and the shape of the sequence.
TensorShape getColShape(const TensorShape& image,
const TensorShape& sequence) {
const TensorShape& sequence) const {
size_t inputChannels = image[1];
size_t inputHeight = image[2];
size_t inputWidth = image[3];
@ -174,8 +183,7 @@ public:
CHECK_EQ(seqLength, outputHeight * outputWidth);
CHECK_EQ(stepSize, inputChannels * blockH() * blockW());
// [output_height, output_width,
// input_channels, filter_height, filter_width]
// [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
return TensorShape({outputHeight,
outputWidth,
inputChannels,
@ -215,40 +223,29 @@ public:
const TensorShape& sequence = outputs[0].shape();
check(image, sequence);
TensorShape imShape = TensorShape({image[1], image[2], image[3]});
TensorShape colShape = getColShape(image, sequence);
size_t batchSize = image[0];
size_t inputChannels = image[1];
size_t inputHeight = image[2];
size_t inputWidth = image[3];
size_t seqLength = sequence[1];
size_t stepSize = sequence[2];
size_t outputHeight = colShape[0];
size_t outputWidth = colShape[1];
real* imageData = inputs[0].data<real>();
real* seqData = outputs[0].data<real>();
Im2ColFunctor<kOCF, Device, real> im2col;
for (size_t i = 0; i < batchSize; i++) {
// The result of im2col is [output_height, output_width,
// input_channels, filter_height, filter_width], and it is easy to
// The result of im2col is [outputHeight, outputWidth,
// inputChannels, filterHeight, filterWidth], and it is easy to
// reshape into [seqLength, stepSize], where seqLength is equal
// output_height * output_width, stepSize is equal
// input_channels * filter_height * filter_width
im2col(imageData,
inputChannels,
inputHeight,
inputWidth,
blockH(),
blockW(),
imShape,
seqData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW(),
outputHeight,
outputWidth,
seqData);
imageData += inputChannels * inputHeight * inputWidth;
seqData += seqLength * stepSize;
paddingW());
imageData += imShape.getElements();
seqData += colShape.getElements();
}
}
};
@ -270,35 +267,24 @@ public:
const TensorShape& sequence = inputs[0].shape();
check(image, sequence);
TensorShape imShape = TensorShape({image[1], image[2], image[3]});
TensorShape colShape = getColShape(image, sequence);
size_t batchSize = image[0];
size_t inputChannels = image[1];
size_t inputHeight = image[2];
size_t inputWidth = image[3];
size_t seqLength = sequence[1];
size_t stepSize = sequence[2];
size_t outputHeight = colShape[0];
size_t outputWidth = colShape[1];
real* imageData = outputs[0].data<real>();
real* seqData = inputs[0].data<real>();
Col2ImFunctor<kOCF, Device, real> col2im;
for (size_t i = 0; i < batchSize; i++) {
col2im(seqData,
inputChannels,
inputHeight,
inputWidth,
blockH(),
blockW(),
col2im(imageData,
imShape,
seqData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW(),
outputHeight,
outputWidth,
imageData);
imageData += inputChannels * inputHeight * inputWidth;
seqData += seqLength * stepSize;
paddingW());
imageData += imShape.getElements();
seqData += colShape.getElements();
}
}
};

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