Feedforward-CNN-Framework/ConvLayer.cpp at master · goodluckcwl/Feedforward-CNN-Framework · GitHub

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* @Author: Weiliang Chen

* @Date: 2016-09-29 10:51:07

* @Last Modified by: Weiliang Chen

* @Last Modified time: 2016-09-29 10:55:27

#include <armadillo>

#include <iostream>

#include "ConvLayer.h"

#include "Blob.h"

#include "common.h"

#include "Layer.h"

#include "Utils.h"

using std::cout;

using std::endl;

namespace fn {

template<typename Dtype>

ConvLayer<Dtype>::ConvLayer()

{

}

template<typename Dtype>

void ConvLayer<Dtype>::LayerSetUp(const Blob<Dtype> & weights, const arma::Col<Dtype> &bias,

const int stride_h, const int stride_w,

const int pad_h, const int pad_w)

{

std::vector<int> shape = weights.shape();

kernel_h_ = shape[0];

kernel_w_ = shape[1];

channels_ = shape[2];

number_ = shape[3];

weights_ = weights;

bias_ = bias;

stride_h_ = stride_h;

stride_w_ = stride_w;

pad_h_ = pad_h;

pad_w_ = pad_w;

}

template<typename Dtype>

void ConvLayer<Dtype>:: Forward(const std::vector<arma::Cube<Dtype>*> &bottom,

std::vector<arma::Cube<Dtype>*> &top) {

if (bottom.size() != 1 ||top.size() != 1 ) {

cout << "Error:The dimension of the input data or output data is wrong." << endl;

return;

}

if (bottom[0]->n_rows > 0

&& bottom[0]->n_cols > 0

&& bottom[0]->n_slices > 0) {

arma::Mat<Dtype> feat_mat;

arma::Mat<Dtype> weights_mat;

bottom[0]->save("input.txt", arma::arma_ascii);

im2col(*(bottom[0]), kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, feat_mat);

feat_mat.save("feat_mat.txt",arma::arma_ascii);

filter2col(*(weights_.data_vec()), weights_mat);

weights_mat.save("weights_mat.txt", arma::arma_ascii);

//conv

arma::Mat<Dtype> conv_mat = feat_mat*weights_mat;

const int output_h = (bottom[0]->n_rows + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;

const int output_w = (bottom[0]->n_cols + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;

conv_mat.save("conv1_mat.txt",arma::arma_ascii);

col2im(conv_mat, output_h, output_w, *(top[0]));

//Add bias

for (int channel = 0; channel < top[0]->n_slices; ++channel) {

top[0]->slice(channel) += bias_.at(channel);

}

template<typename Dtype>

void ConvLayer<Dtype>::Forward(const arma::Cube<Dtype>& bottom, arma::Cube<Dtype>& top)

{

if (bottom.n_rows > 0

&& bottom.n_cols > 0

&& bottom.n_slices > 0) {

arma::Mat<Dtype> feat_mat;

arma::Mat<Dtype> weights_mat;

im2col(bottom, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, feat_mat);

filter2col(*(weights_.data_vec()), weights_mat);

// printMat(feat_mat, "feat_mat.txt");

// printMat(weights_mat, "weights_mat.txt");

//conv

arma::Mat<Dtype> conv_mat = feat_mat*weights_mat;

const int output_h = (bottom.n_rows + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;

const int output_w = (bottom.n_cols + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;

col2im(conv_mat, output_h, output_w, top);

//Add bias

for (int channel = 0; channel < top.n_slices; ++channel) {

top.slice(channel) += bias_.at(channel);

}

template<typename Dtype>

void ConvLayer<Dtype>::CalShape(const arma::Cube<Dtype>& bottom,

std::vector<int>& shape)

{

const int output_h = (bottom.n_rows + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;

const int output_w = (bottom.n_cols + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;

shape[0] = output_h;

shape[1] = output_w;

shape[2] = number_;

}

template<typename Dtype>

ConvLayer<Dtype>::~ConvLayer() {

}

//Explicit instantiation

INSTANTIATE_CLASS(ConvLayer);

}

Feedforward-CNN-Framework/ConvLayer.cpp at master · goodluckcwl/Feedforward-CNN-Framework · GitHub

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