Caffe源码（五）：conv_layer 分析

简单介绍

首先要明确的一点是：ConvolutionLayer 是 BaseConvolutionLayer的子类，BaseConvolutionLayer 是 Layer 的子类。ConvolutionLayer 除了继承了相应的成员变量和函数以外，自己的成员函数主要有：compute_output_shape，Forward_cpu，Backward_cpu 。

主要函数

1. compute_output_shape 函数：

计算输出feature map 的shape。

template <typename Dtype>void ConvolutionLayer<Dtype>::compute_output_shape() {  this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)      / this->stride_h_ + 1;  //输出feature map 的 height  this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)      / this->stride_w_ + 1;  //输出 feature map 的 width }

2.Forward_cpu 函数：

该函数在Layer 中声明，实现前向传播功能。

template <typename Dtype>void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  const Dtype* weight = this->blobs_[0]->cpu_data();   //blobs_ 用来存储可学习的参数blobs_[0] 是weight，blobs_[1]是bias  for (int i = 0; i < bottom.size(); ++i) {   //这里的i为输入bottom的个数，输入多少个bottom就产生相应个数的输出 top。    const Dtype* bottom_data = bottom[i]->cpu_data();    Dtype* top_data = top[i]->mutable_cpu_data();    for (int n = 0; n < this->num_; ++n) {      this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,          top_data + top[i]->offset(n));//计算卷积操作之后的输出      if (this->bias_term_) {        const Dtype* bias = this->blobs_[1]->cpu_data();        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);      }//加上bias    }  }}

Layer的构造函数

explicit Layer(const LayerParameter& param)    : layer_param_(param) {      // Set phase and copy blobs (if there are any).      phase_ = param.phase();      if (layer_param_.blobs_size() > 0) {        blobs_.resize(layer_param_.blobs_size());        for (int i = 0; i < layer_param_.blobs_size(); ++i) {          blobs_[i].reset(new Blob<Dtype>());          blobs_[i]->FromProto(layer_param_.blobs(i));        }      }    }//用从protobuf 读入message LayerParameter 中的blobs 初始化 blobs_      //blobs_定义：vector<shared_ptr<Blob<Dtype> > > blobs_

3.Backward_cpu 函数

实现反向传播，根据上一层传下来的导数计算相应的bottom data ， weight， bias 的导数

template <typename Dtype>void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {  const Dtype* weight = this->blobs_[0]->cpu_data();  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();  if (this->param_propagate_down_[0]) {    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);  }  if (this->bias_term_ && this->param_propagate_down_[1]) {    caffe_set(this->blobs_[1]->count(), Dtype(0),        this->blobs_[1]->mutable_cpu_diff());  }  for (int i = 0; i < top.size(); ++i) {    const Dtype* top_diff = top[i]->cpu_diff();//上一层传下来的导数    const Dtype* bottom_data = bottom[i]->cpu_data();    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();//传给下一层的导数    // Bias gradient, if necessary.    if (this->bias_term_ && this->param_propagate_down_[1]) {      Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();      for (int n = 0; n < this->num_; ++n) {        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));      }    }    if (this->param_propagate_down_[0] || propagate_down[i]) {      for (int n = 0; n < this->num_; ++n) {        // gradient w.r.t. weight. Note that we will accumulate diffs.        if (this->param_propagate_down_[0]) {          this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n),              top_diff + top[i]->offset(n), weight_diff);        }//对weight 计算导数（用来更新weight）        // gradient w.r.t. bottom data, if necessary.        if (propagate_down[i]) {          this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight,              bottom_diff + bottom[i]->offset(n));        }//对bottom数据计算导数（传给下一层）      }    }  }}

转载地址：http://blog.csdn.net/seven_first/article/details/47665817