hedaoyuan
8 years ago
parent
1b8d2e65ae
commit
48e0f43253
@ -0,0 +1,84 @@
|
||||
/* 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
|
||||
|
||||
// #include "ConvOp.h"
|
||||
|
||||
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 four dimensions(NCHW) into a colData.
|
||||
* Then you can reshape colData to a convolution matrix for
|
||||
* convolution calculation based on matrix multiplication.
|
||||
*
|
||||
* \param imData Image data of NCHW format.
|
||||
* The format of imData is:
|
||||
* [input_channels, input_height, input_width].
|
||||
* \param colData colData data.
|
||||
* If the template argument Format is kCFO,
|
||||
* the format of colData is:
|
||||
* [input_channels,
|
||||
* filter_height,
|
||||
* filter_width,
|
||||
* output_height,
|
||||
* output_width]
|
||||
* If the template argument Format is kOCF,
|
||||
* the format of colData is:
|
||||
* [output_height,
|
||||
* output_width,
|
||||
* input_channels,
|
||||
* filter_height,
|
||||
* filter_width]
|
||||
*/
|
||||
template <ColFormat Format, DeviceType Device, class T>
|
||||
class Im2ColFunctor {
|
||||
public:
|
||||
void operator()(const T* imData,
|
||||
int inputChannels,
|
||||
int inputHeight,
|
||||
int inputWidth,
|
||||
int filterHeight,
|
||||
int filterWidth,
|
||||
int strideHeight,
|
||||
int strideWidth,
|
||||
int paddingHeight,
|
||||
int paddingWidth,
|
||||
int outputHeight,
|
||||
int outputWidth,
|
||||
T* colData);
|
||||
};
|
||||
|
||||
template <ColFormat Format, DeviceType Device, class T>
|
||||
class Col2ImFunctor {
|
||||
public:
|
||||
void operator()(const T* colData,
|
||||
int inputChannels,
|
||||
int inputHeight,
|
||||
int inputWidth,
|
||||
int filterHeight,
|
||||
int filterWidth,
|
||||
int strideHeight,
|
||||
int strideWidth,
|
||||
int paddingHeight,
|
||||
int paddingWidth,
|
||||
int outputHeight,
|
||||
int outputWidth,
|
||||
T* imData);
|
||||
};
|
||||
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,164 @@
|
||||
/* 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. */
|
||||
|
||||
#include "Function.h"
|
||||
#include "GemmConvOp.h"
|
||||
|
||||
namespace paddle {
|
||||
|
||||
/*
|
||||
* imData = [input_channels, input_height, input_width]
|
||||
* colData = [output_height, output_width,
|
||||
* input_channels, filter_height, filter_width]
|
||||
*/
|
||||
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,
|
||||
int strideHeight,
|
||||
int strideWidth,
|
||||
int paddingHeight,
|
||||
int paddingWidth,
|
||||
int outputHeight,
|
||||
int outputWidth,
|
||||
T* colData) {
|
||||
for (int outputH = 0; outputH < outputHeight; ++outputH) {
|
||||
for (int outputW = 0; outputW < outputWidth; ++outputW) {
|
||||
for (int channel = 0; channel < inputChannels; ++channel) {
|
||||
for (int filterH = 0; filterH < filterHeight; ++filterH) {
|
||||
for (int filterW = 0; filterW < filterWidth; ++filterW) {
|
||||
int imRowOffset =
|
||||
outputH * strideHeight + filterH - paddingHeight;
|
||||
int imColOffset = outputW * strideWidth + filterW - paddingWidth;
|
||||
int colDataOffset =
|
||||
(((outputH * outputWidth + outputW) * inputChannels +
|
||||
channel) *
|
||||
filterHeight +
|
||||
filterH) *
|
||||
filterWidth +
|
||||
filterW;
|
||||
if (imRowOffset < 0 || imRowOffset >= inputHeight ||
|
||||
imColOffset < 0 || imColOffset >= inputWidth) {
|
||||
colData[colDataOffset] = T(0);
|
||||
} else {
|
||||
int imDataOffset =
|
||||
(channel * inputHeight + imRowOffset) * inputWidth +
|
||||
imColOffset;
|
||||
colData[colDataOffset] = imData[imDataOffset];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/*
|
||||
* \brief Converts the image data of four dimensions(NCHW) into
|
||||
* a sequence data of three dimensions(NST). Where N is batch size,
|
||||
* S is the length of the sequence after each image is expanded,
|
||||
* T is the size of each time step in the sequence.
|
||||
*
|
||||
* \param inputs[0] Image data of NCHW format.
|
||||
* \param outputs[0] Sequence data of NST format.
|
||||
*/
|
||||
template <DeviceType Device>
|
||||
class ImageExpandFunction : public FunctionBase {
|
||||
public:
|
||||
void init(const FuncConfig& config) override {
|
||||
// function arguments
|
||||
strides_ = config.get<std::vector<size_t>>("strides");
|
||||
paddings_ = config.get<std::vector<size_t>>("paddings");
|
||||
blocks_ = config.get<std::vector<size_t>>("blocks");
|
||||
|
||||
// number of inputs and outputs
|
||||
numInputs_ = 1;
|
||||
numOutputs_ = 1;
|
||||
}
|
||||
|
||||
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
|
||||
CHECK_EQ(numInputs_, inputs.size());
|
||||
CHECK_EQ(numOutputs_, outputs.size());
|
||||
const TensorShape& input = inputs[0].shape();
|
||||
const TensorShape& output = outputs[0].shape();
|
||||
// input argument should be 4-dimensional.
|
||||
CHECK_EQ(input.ndims(), (size_t)4);
|
||||
// output argument should be 3-dimensional.
|
||||
CHECK_EQ(output.ndims(), (size_t)3);
|
||||
// The batchSize of the input needs to be equal to
|
||||
// the batchSize of the output.
|
||||
CHECK_EQ(input[0], output[0]);
|
||||
|
||||
size_t batchSize = input[0];
|
||||
size_t inputChannels = input[1];
|
||||
size_t inputHeight = input[2];
|
||||
size_t inputWidth = input[3];
|
||||
size_t seqLength = output[1];
|
||||
size_t stepSize = output[2];
|
||||
size_t outputHeight =
|
||||
1 +
|
||||
(inputHeight + 2 * paddingH() - blockH() + strideH() - 1) / strideH();
|
||||
size_t outputWidth =
|
||||
1 +
|
||||
(inputWidth + 2 * paddingW() - blockW() + strideW() - 1) / strideW();
|
||||
CHECK_EQ(seqLength, outputHeight * outputWidth);
|
||||
CHECK_EQ(stepSize, inputChannels * blockH() * blockH());
|
||||
|
||||
real* inputData = inputs[0].data<real>();
|
||||
real* outputData = outputs[0].data<real>();
|
||||
Im2ColFunctor<kOCF, Device, real> im2col;
|
||||
for (size_t i = 0; i < batchSize; i++) {
|
||||
im2col(inputData,
|
||||
inputChannels,
|
||||
inputHeight,
|
||||
inputWidth,
|
||||
blockH(),
|
||||
blockW(),
|
||||
strideH(),
|
||||
strideW(),
|
||||
paddingH(),
|
||||
paddingW(),
|
||||
outputHeight,
|
||||
outputWidth,
|
||||
outputData);
|
||||
inputData += inputChannels * inputHeight * inputWidth;
|
||||
outputData += seqLength * stepSize;
|
||||
}
|
||||
}
|
||||
|
||||
protected:
|
||||
std::vector<size_t> strides_;
|
||||
std::vector<size_t> paddings_;
|
||||
std::vector<size_t> blocks_;
|
||||
|
||||
inline int strideH() const { return strides_[0]; }
|
||||
|
||||
inline int strideW() const { return strides_[1]; }
|
||||
|
||||
inline int paddingH() const { return paddings_[0]; }
|
||||
|
||||
inline int paddingW() const { return paddings_[1]; }
|
||||
|
||||
inline int blockH() const { return blocks_[0]; }
|
||||
|
||||
inline int blockW() const { return blocks_[1]; }
|
||||
};
|
||||
|
||||
} // namespace paddle
|
||||
Loading…
Reference in new issue