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103 lines
3.7 KiB
103 lines
3.7 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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#pragma once
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#include "paddle/framework/tensor.h"
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#include "paddle/platform/device_context.h"
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namespace paddle {
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namespace operators {
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namespace math {
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/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */
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enum class ColFormat { kCFO = 0, kOCF = 1 };
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/*
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* \brief Converts the image data of three dimensions(CHW) into a colData of
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* five dimensions in the Im2ColFunctor calculation,
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* And in the Col2ImFunctor calculation, it is reversed.
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*
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* \param imData Image data.
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* \param imShape The shape of imData,
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* [input_channels, input_height, input_width].
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* \param colData Column data.
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* \param colShape The shape of colData.
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*
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* \param dilations dilation data.
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* \param 2-dimension [dilation_height, dilation_width].
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*
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* \param strides stride data.
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* \param 2-dimension [stride_height, stride_width].
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*
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* \param paddings padding data.
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* \param 4-dimension [up_pad, left_pad, down_pad, right_pad].
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*
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* If the template argument Format is kCFO, the shape of colData is:
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* [input_channels, filter_height, filter_width, output_height, output_width]
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* So, it is easy to reshape into a convolution matrix for convolution
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* calculation based on matrix multiplication.
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* The shape of convolution matrix is [height, width], where the height is equal
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* input_channels * filter_height * filter_width, and the width is equal
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* output_height * output_width.
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*
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* Reshape:
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* shape of colData shape of convolution matrix
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* [input_channels,
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* filter_height,
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* filter_width, ======> [height, width]
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* output_height,
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* output_width]
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*
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* If the template argument Format is kOCF, the shape of colData is:
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* [output_height, output_width, input_channels, filter_height, filter_width]
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* So, it is easy to reshape into a sequence matrix for rnn calculation.
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* The shape of sequence matrix is [seq_length, step_size], where the seq_length
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* is equal output_height * output_width, and the step_size is equal
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* input_channels * filter_height * filter_width.
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*
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* Reshape:
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* shape of colData shape of sequence matrix
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* [output_height,
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* output_width,
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* input_channels, ======> [seqLength, stepSize]
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* filter_height,
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* filter_width]
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*
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* \note The caller needs to ensure that imShape.inputChannels is equal to
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* colShape.inputChannels.
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*/
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template <ColFormat Format, typename Place, typename T>
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class Im2ColFunctor {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& im, const std::vector<int>& dilation,
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const std::vector<int>& stride,
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const std::vector<int>& padding, framework::Tensor* col);
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};
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template <ColFormat Format, typename Place, typename T>
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class Col2ImFunctor {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& col,
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const std::vector<int>& dilation,
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const std::vector<int>& stride,
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const std::vector<int>& padding, framework::Tensor* im);
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};
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} // namespace math
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} // namespace operators
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} // namespace paddle
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