Caffe源码(五):conv_layer 分析

发布于:2021-06-22 23:49:02

目录




目录简单介绍主要函数
compute_output_shape 函数Forward_cpu 函数Backward_cpu 函数



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
void ConvolutionLayer::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
void ConvolutionLayer::Forward_cpu(const vector*>& bottom,
const vector*>& top) {
const Dtype* weight = this->blobs_[0]->cpu_data();
//blobs_ 用来存储可学*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());
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 > > 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
void ConvolutionLayer::Backward_cpu(const vector*>& top,
const vector& propagate_down, const vector*>& 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)
}
}
}
}

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