Caffe源码-Layer类
Layer类简介
Layer是caffe中搭建网络的基本单元，caffe代码中包含大量Layer基类派生出来的各种各样的层，各自通过虚函数 Forward() 和 Backward() 实现自己的功能。 Forward() 函数用于前向计算过程，由 bottom blob 计算 top blob 和 loss ，实现数据由浅至深的传递。而 Backward() 函数用于反向传播过程，由 top blob 的计算 bottom blob 的梯度，将网络的预测误差向浅层网络传递，以便更新网络的参数。
layer.hpp源码
/**
* @brief An interface for the units of computation which can be composed into a
* Net.
*
* Layer%s must implement a Forward function, in which they take their input
* (bottom) Blob%s (if any) and compute their output Blob%s (if any).
* They may also implement a Backward function, in which they compute the error
* gradients with respect to their input Blob%s, given the error gradients with
* their output Blob%s.
*/
template <typename Dtype>
class Layer {
public:
/**
* You should not implement your own constructor. Any set up code should go
* to SetUp(), where the dimensions of the bottom blobs are provided to the
* layer.
*/
explicit Layer(const LayerParameter& param) //构造函数,根据LayerParameter初始化当前的layer
: layer_param_(param) { //先保存param
// Set phase and copy blobs (if there are any).
phase_ = param.phase(); //设置layer的状态(TRAIN or TEST)
if (layer_param_.blobs_size() > 0) { //如果layer中存在blob参数(可学习参数)
blobs_.resize(layer_param_.blobs_size()); //调整blobs_的大小
for (int i = 0; i < layer_param_.blobs_size(); ++i) {
blobs_[i].reset(new Blob<Dtype>()); //指针指向新创建的Blob数据
blobs_[i]->FromProto(layer_param_.blobs(i)); //读取layer_param_中的BlobProto类型的数据,存入blob中
}
}
}
virtual ~Layer() {}
/**
* @brief Implements common layer setup functionality.
*
* @param bottom the preshaped input blobs
* @param top
* the allocated but unshaped output blobs, to be shaped by Reshape
*
* Checks that the number of bottom and top blobs is correct.
* Calls LayerSetUp to do special layer setup for individual layer types,
* followed by Reshape to set up sizes of top blobs and internal buffers.
* Sets up the loss weight multiplier blobs for any non-zero loss weights.
* This method may not be overridden.
*/
void SetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
CheckBlobCounts(bottom, top); //检查输入输出的blob的个数是否正确
LayerSetUp(bottom, top); //虚函数,layer创建时做一些初始化工作,每个子类根据需要自己实现
Reshape(bottom, top); //虚函数,调整输出blob的形状和layer中内部缓冲区的大小
SetLossWeights(top); //设置输出blob的权重
}
/**
* @brief Does layer-specific setup: your layer should implement this function
* as well as Reshape.
*
* @param bottom
* the preshaped input blobs, whose data fields store the input data for
* this layer
* @param top
* the allocated but unshaped output blobs
*
* This method should do one-time layer specific setup. This includes reading
* and processing relevent parameters from the <code>layer_param_</code>.
* Setting up the shapes of top blobs and internal buffers should be done in
* <code>Reshape</code>, which will be called before the forward pass to
* adjust the top blob sizes.
*/
//特定层有特定的实现方式,一般用于在layer创建时做一些一次性的操作,如从layer_param_中读取点参数之类的
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {}
/**
* @brief Adjust the shapes of top blobs and internal buffers to accommodate
* the shapes of the bottom blobs.
*
* @param bottom the input blobs, with the requested input shapes
* @param top the top blobs, which should be reshaped as needed
*
* This method should reshape top blobs as needed according to the shapes
* of the bottom (input) blobs, as well as reshaping any internal buffers
* and making any other necessary adjustments so that the layer can
* accommodate the bottom blobs.
*/
//根据输入blob的形状,调整输出blob的形状和调整内部的缓冲区.该函数会在每次前向计算之前被调用
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) = 0;
/**
* @brief Given the bottom blobs, compute the top blobs and the loss.
*
* @param bottom
* the input blobs, whose data fields store the input data for this layer
* @param top
* the preshaped output blobs, whose data fields will store this layers'
* outputs
* \return The total loss from the layer.
*
* The Forward wrapper calls the relevant device wrapper function
* (Forward_cpu or Forward_gpu) to compute the top blob values given the
* bottom blobs. If the layer has any non-zero loss_weights, the wrapper
* then computes and returns the loss.
*
* Your layer should implement Forward_cpu and (optionally) Forward_gpu.
*/
//前向计算函数,传入输入blob数据,计算输出blob,和loss值(权重参数非0时).
inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
/**
* @brief Given the top blob error gradients, compute the bottom blob error
* gradients.
*
* @param top
* the output blobs, whose diff fields store the gradient of the error
* with respect to themselves
* @param propagate_down
* a vector with equal length to bottom, with each index indicating
* whether to propagate the error gradients down to the bottom blob at
* the corresponding index
* @param bottom
* the input blobs, whose diff fields will store the gradient of the error
* with respect to themselves after Backward is run
*
* The Backward wrapper calls the relevant device wrapper function
* (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
* top blob diffs.
*
* Your layer should implement Backward_cpu and (optionally) Backward_gpu.
*/
//给出输出blob数据的误差梯度,计算输入blob数据的误差梯度
inline void Backward(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, //个数与bottom.size()相等,对应每个输入blob是否需要反向传播
const vector<Blob<Dtype>*>& bottom);
/**
* @brief Returns the vector of learnable parameter blobs.
*/
vector<shared_ptr<Blob<Dtype> > >& blobs() { //返回layer中内部可学习的参数的个数
return blobs_;
}
/**
* @brief Returns the layer parameter.
*/
const LayerParameter& layer_param() const { return layer_param_; } //返回layer的配置参数
/**
* @brief Writes the layer parameter to a protocol buffer
*/
virtual void ToProto(LayerParameter* param, bool write_diff = false); //将layer的blob数据写入param中
/**
* @brief Returns the scalar loss associated with a top blob at a given index.
*/
inline Dtype loss(const int top_index) const {
return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0); //返回第top_index个输出blob对应的loss权重
}
/**
* @brief Sets the loss associated with a top blob at a given index.
*/
inline void set_loss(const int top_index, const Dtype value) { //设置第top_index个输出blob对应的loss的权重为value
if (loss_.size() <= top_index) {
loss_.resize(top_index + 1, Dtype(0)); //个数小于top_index,则调整大小,并用Dtype(0)初始化一下
}
loss_[top_index] = value; //对应的loss权重设置为value
}
/**
* @brief Returns the layer type.
*/
virtual inline const char* type() const { return ""; } //返回layer的类型
/**
* @brief Returns the exact number of bottom blobs required by the layer,
* or -1 if no exact number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some exact number of bottom blobs.
*/
virtual inline int ExactNumBottomBlobs() const { return -1; } //layer中要求输入blob的准确个数,-1表示无要求
/**
* @brief Returns the minimum number of bottom blobs required by the layer,
* or -1 if no minimum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some minimum number of bottom blobs.
*/
virtual inline int MinBottomBlobs() const { return -1; } //layer中要求输入blob的最小个数,-1表示无要求
/**
* @brief Returns the maximum number of bottom blobs required by the layer,
* or -1 if no maximum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some maximum number of bottom blobs.
*/
virtual inline int MaxBottomBlobs() const { return -1; } //layer中要求输入blob的最大个数,-1表示无要求
/**
* @brief Returns the exact number of top blobs required by the layer,
* or -1 if no exact number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some exact number of top blobs.
*/
virtual inline int ExactNumTopBlobs() const { return -1; } //layer中要求输出blob的准确个数,-1表示无要求
/**
* @brief Returns the minimum number of top blobs required by the layer,
* or -1 if no minimum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some minimum number of top blobs.
*/
virtual inline int MinTopBlobs() const { return -1; } //layer中要求输出blob的最小个数,-1表示无要求
/**
* @brief Returns the maximum number of top blobs required by the layer,
* or -1 if no maximum number is required.
*
* This method should be overridden to return a non-negative value if your
* layer expects some maximum number of top blobs.
*/
virtual inline int MaxTopBlobs() const { return -1; } //layer中要求输出blob的最大个数,-1表示无要求
/**
* @brief Returns true if the layer requires an equal number of bottom and
* top blobs.
*
* This method should be overridden to return true if your layer expects an
* equal number of bottom and top blobs.
*/
virtual inline bool EqualNumBottomTopBlobs() const { return false; } //layer中要求输入blob的个数与输出blob的个数是否相等
/**
* @brief Return whether "anonymous" top blobs are created automatically
* by the layer.
*
* If this method returns true, Net::Init will create enough "anonymous" top
* blobs to fulfill the requirement specified by ExactNumTopBlobs() or
* MinTopBlobs().
*/
//是否允许自动创建匿名blob.是则在Net::Init()中会自动创建匿名输出blob,直至其个数达到ExactNumTopBlobs()和MinTopBlobs()的最大值
virtual inline bool AutoTopBlobs() const { return false; } //TODO 匿名blob的用途暂时还不了解
/**
* @brief Return whether to allow force_backward for a given bottom blob
* index.
*
* If AllowForceBackward(i) == false, we will ignore the force_backward
* setting and backpropagate to blob i only if it needs gradient information
* (as is done when force_backward == false).
*/
//允许的话则当net设置了强制反传时,layer的每个输出blob都必须遵从net的设置.如果不允许的话,则layer的输出blob是否需要反向传播
//仍是根据自身的设置来决定,不必考虑net的设置.具体可见 net.cpp -> Init() -> if(param.force_backward())...
virtual inline bool AllowForceBackward(const int bottom_index) const { //layer是否允许强制反向传播
return true;
}
/**
* @brief Specifies whether the layer should compute gradients w.r.t. a
* parameter at a particular index given by param_id.
*
* You can safely ignore false values and always compute gradients
* for all parameters, but possibly with wasteful computation.
*/
inline bool param_propagate_down(const int param_id) { //返回layer中第param_id个参数blob是否需要计算梯度
return (param_propagate_down_.size() > param_id) ?
param_propagate_down_[param_id] : false; //超出param_id返回为false
}
/**
* @brief Sets whether the layer should compute gradients w.r.t. a
* parameter at a particular index given by param_id.
*/
inline void set_param_propagate_down(const int param_id, const bool value) { //设置layer中第param_id个参数blob是否需要计算梯度
if (param_propagate_down_.size() <= param_id) {
param_propagate_down_.resize(param_id + 1, true); //调整大小
}
param_propagate_down_[param_id] = value; //设置值为value
}
protected:
/** The protobuf that stores the layer parameters */
//layer_param_中存放着layer中的各种参数,不过使用了protobuf库,例如layer中的blobs_是以BlobProto类型存放在layer_param_中
LayerParameter layer_param_; //layer的配置参数,protobuf消息类型
/** The phase: TRAIN or TEST */
Phase phase_; //layer的字段,训练模式(TRAIN)还是测试模式(TEST)
/** The vector that stores the learnable parameters as a set of blobs. */
vector<shared_ptr<Blob<Dtype> > > blobs_; //layer中的可学习参数(如卷积层的权重,偏置参数都是以此类型存放)
/** Vector indicating whether to compute the diff of each param blob. */
vector<bool> param_propagate_down_; //只是每个参数blob是否需要计算梯度
/** The vector that indicates whether each top blob has a non-zero weight in
* the objective function. */
vector<Dtype> loss_; //个数与top.size()相等,表示每个top blob在计算loss时的权重,非loss layer中默认都是0
/** @brief Using the CPU device, compute the layer output. */
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) = 0; //使用cpu进行前向计算
/**
* @brief Using the GPU device, compute the layer output.
* Fall back to Forward_cpu() if unavailable.
*/
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) { //使用gpu进行前向计算
// LOG(WARNING) << "Using CPU code as backup.";
return Forward_cpu(bottom, top); //子类中未实现则默认调用Forward_cpu()
}
/**
* @brief Using the CPU device, compute the gradients for any parameters and
* for the bottom blobs if propagate_down is true.
*/
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) = 0; //使用cpu进行反向计算
/**
* @brief Using the GPU device, compute the gradients for any parameters and
* for the bottom blobs if propagate_down is true.
* Fall back to Backward_cpu() if unavailable.
*/
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) { //使用gpu进行反向计算
// LOG(WARNING) << "Using CPU code as backup.";
Backward_cpu(top, propagate_down, bottom); //未实现则调用Backward_cpu()
}
/**
* Called by the parent Layer's SetUp to check that the number of bottom
* and top Blobs provided as input match the expected numbers specified by
* the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
*/
virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) { //检查输入输出blob的个数是否符合要求
if (ExactNumBottomBlobs() >= 0) {
CHECK_EQ(ExactNumBottomBlobs(), bottom.size()) //指定了输入blob的个数,检查是否相等
<< type() << " Layer takes " << ExactNumBottomBlobs()
<< " bottom blob(s) as input.";
}
if (MinBottomBlobs() >= 0) {
CHECK_LE(MinBottomBlobs(), bottom.size()) //指定了输入blob的最小个数,检查是否小于等于
<< type() << " Layer takes at least " << MinBottomBlobs()
<< " bottom blob(s) as input.";
}
if (MaxBottomBlobs() >= 0) {
CHECK_GE(MaxBottomBlobs(), bottom.size()) //指定了输入blob的最大个数,检查是否大于等于
<< type() << " Layer takes at most " << MaxBottomBlobs()
<< " bottom blob(s) as input.";
}
if (ExactNumTopBlobs() >= 0) {
CHECK_EQ(ExactNumTopBlobs(), top.size()) //指定了输出blob的个数,检查是否相等
<< type() << " Layer produces " << ExactNumTopBlobs()
<< " top blob(s) as output.";
}
if (MinTopBlobs() >= 0) {
CHECK_LE(MinTopBlobs(), top.size()) //指定了输出blob的最小个数,检查是否小于等于
<< type() << " Layer produces at least " << MinTopBlobs()
<< " top blob(s) as output.";
}
if (MaxTopBlobs() >= 0) {
CHECK_GE(MaxTopBlobs(), top.size()) //指定了输出blob的最大个数,检查是否大于等于
<< type() << " Layer produces at most " << MaxTopBlobs()
<< " top blob(s) as output.";
}
if (EqualNumBottomTopBlobs()) {
CHECK_EQ(bottom.size(), top.size()) //指定了输入输出blob的个数必须相等,检查是否相等
<< type() << " Layer produces one top blob as output for each "
<< "bottom blob input.";
}
}
/**
* Called by SetUp to initialize the weights associated with any top blobs in
* the loss function. Store non-zero loss weights in the diff blob.
*/
inline void SetLossWeights(const vector<Blob<Dtype>*>& top) { //为每个输出blob设置对应的权重
const int num_loss_weights = layer_param_.loss_weight_size(); //layer_param_中设置的loss weight的个数
if (num_loss_weights) {
CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
"unspecified or specified once per top blob."; //检查loss权重个数与输出blob个数是否相等
for (int top_id = 0; top_id < top.size(); ++top_id) {
const Dtype loss_weight = layer_param_.loss_weight(top_id); //第top_id个输出blob对应的loss权重
if (loss_weight == Dtype(0)) { continue; } //loss权重为0,直接跳过
this->set_loss(top_id, loss_weight); //权重保存在loss_中,loss_[top_id]=loss_weight
const int count = top[top_id]->count(); //第top_id个输出blob的数据的个数
Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff(); //第top_id个输出blob的diff的数据指针
caffe_set(count, loss_weight, loss_multiplier); //将输出blob的diff数据设置为对应的权重,loss_multiplier[i]=loss_weight,i=[0,count)
}
}
}
private:
DISABLE_COPY_AND_ASSIGN(Layer);
}; // class Layer
// Forward and backward wrappers. You should implement the cpu and
// gpu specific implementations instead, and should not change these
// functions.
template <typename Dtype>
inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom, //前向计算过程,由输入blob计算输出blob和对应的loss
const vector<Blob<Dtype>*>& top) {
Dtype loss = 0;
Reshape(bottom, top); //先调整输出blob和内部缓冲区的形状
switch (Caffe::mode()) { //当前caffe运行的模式
case Caffe::CPU:
Forward_cpu(bottom, top); //cpu模式下,调用Forward_cpu()执行前向计算
for (int top_id = 0; top_id < top.size(); ++top_id) { //每个输出blob数据
if (!this->loss(top_id)) { continue; } //loss权重为0,跳过
const int count = top[top_id]->count(); //blob数据的个数
const Dtype* data = top[top_id]->cpu_data(); //blob数据的cpu指针
const Dtype* loss_weights = top[top_id]->cpu_diff(); //blob数据的loss权重
//loss_weights只有在loss layer中才是非0值.而loss layer中的top blob的data_数据就是误差值,即此处的data存放layer的误差,点乘得到总的loss
loss += caffe_cpu_dot(count, data, loss_weights); //data点乘loss_weights,得到loss值
}
break;
case Caffe::GPU: //caffe运行在gpu模式下
Forward_gpu(bottom, top); //调用Forward_gpu()执行前向计算过程
#ifndef CPU_ONLY
for (int top_id = 0; top_id < top.size(); ++top_id) { //与上类似,提取输出blob的gpu数据并计算总的loss值
if (!this->loss(top_id)) { continue; }
const int count = top[top_id]->count();
const Dtype* data = top[top_id]->gpu_data();
const Dtype* loss_weights = top[top_id]->gpu_diff();
Dtype blob_loss = 0;
caffe_gpu_dot(count, data, loss_weights, &blob_loss);
loss += blob_loss;
}
#endif
break;
default:
LOG(FATAL) << "Unknown caffe mode.";
}
return loss; //返回loss值
}
template <typename Dtype>
inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) { //执行反向传播过程
switch (Caffe::mode()) { //根据模式调用相应的处理函数
case Caffe::CPU:
Backward_cpu(top, propagate_down, bottom);
break;
case Caffe::GPU:
Backward_gpu(top, propagate_down, bottom);
break;
default:
LOG(FATAL) << "Unknown caffe mode.";
}
}
// Serialize LayerParameter to protocol buffer
//将layer中的参数blob写入到LayerParameter中的BlobProto类型的消息中
template <typename Dtype>
void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
param->Clear(); //清空param中的所有消息数据
param->CopyFrom(layer_param_); //将layer中的消息layer_param_拷贝至param中
param->clear_blobs(); //清空param中的BlobProto消息数据
for (int i = 0; i < blobs_.size(); ++i) {
//调用blob.cpp中的Blob<double/float>::ToProto()函数,将blob数据以BlobProto消息格式写入param中
blobs_[i]->ToProto(param->add_blobs(), write_diff);
}
}
小结
- Layer类中的成员
vector<Dtype> loss_;，loss_中存放的值是输出top blob对应的loss权重，而不是实际的loss值，变量名好像有点误导性。 - ToProto()函数中先是进行
param->clear_blobs()操作，再进行blobs_[i]->ToProto()操作。在创建Layer类的时候，layer中的参数blob是从layer_param_的BlobProto类型的消息中读取进来的（blobs_[i]->FromProto(layer_param_.blobs(i))），此时在Layer类中，LayerParameter中的BlobProto类型的变量与Blob类型的变量的数据内容是一样的。但是随着网络的训练，Blob类型的变量（可学习参数）会不断更新，而LayerParameter中的BlobProto中的数据并不会更新，数据并不一致，所以在ToProto()函数中需要先清除旧的BlobProto数据，将Blob类型数据转化为BlobProto类型存入其中。
Caffe的源码笔者是第一次阅读，一边阅读一边记录，对代码的理解和分析可能会存在错误或遗漏，希望各位读者批评指正，谢谢支持！
