Caffe的整体流程图：

程序入口：main()

1 int main(int argc, char** argv) {
2       .....
3       return GetBrewFunction(caffe::string(argv[1]))();
4       ....
5 }

g_brew_map实现过程，首先通过 typedef定义函数指针 typedef int (*BrewFunction)(); 这个是用typedef定义函数指针方法。这个程序定义一个BrewFunction函数指针类型，在caffe.cpp 中 BrewFunction 作为GetBrewFunction()函数的返回类型，可以是 train()，test()，device_query()，time() 这四个函数指针的其中一个。在train()，test()，中可以调用solver类的函数，从而进入到net，进入到每一层，运行整个caffe程序。然后对每个函数注册。

1 RegisterBrewFunction(train)
2 RegisterBrewFunction(test)
3 RegisterBrewFunction(device_query)
4 RegisterBrewFunction(time)

train: 训练或者调整一个模型
test : 在测试集上测试一个模型
device_query : 打印GPU的调试信息
time: 压测一个模型的执行时间

如果需要，可以增加其他的方式，然后通过RegisterBrewFunction()函数注册一下即可。

接着调用train()函数，train函数中主要有三个方法ReadSolverParamsFromTextFileOrDie、CreateSolver、Solve。

 1 // Train / Finetune a model.
 2 int train() {
 3   ......
 4   caffe::SolverParameter solver_param;
 5   caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);//从-solver参数读取solver_param
 6   ......
 7   shared_ptr<caffe::Solver<float> >
 8       solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));//从参数创建solver，同样采用string到函数指针的映射实现，用到了工厂模式
 9 
10   if (FLAGS_snapshot.size()) {//迭代snapshot次后保存模型一次
11     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
12     solver->Restore(FLAGS_snapshot.c_str());
13   } else if (FLAGS_weights.size()) {//若采用finetuning，则拷贝weight到指定模型
14     CopyLayers(solver.get(), FLAGS_weights);
15   }
16 
17   if (gpus.size() > 1) {
18     caffe::P2PSync<float> sync(solver, NULL, solver->param());
19     sync.Run(gpus);
20   } else {
21     LOG(INFO) << "Starting Optimization";
22     solver->Solve();//开始训练网络
23   }
24   LOG(INFO) << "Optimization Done.";
25   return 0;
26 }

ReadSolverParamsFromTextFileOrDie

caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param)解析-solver指定的solver.prototxt的文件内容到solver_param中

CreateSolver

CreateSolver函数构建solver和net，该函数是初始化的入口，会通过执行Solver的构造函数，调用 void Solver<Dtype>::Init(const SolverParameter& param)，该函数内有InitTrainNet()、InitTestNets()。对于InitTrainNet函数：

......
net_.reset(new Net<Dtype>(net_param));

调用Net类的构造函数，然后执行Init()操作，该函数具体的内容如下图和源码所示：

 1 template <typename Dtype>
 2 void Net<Dtype>::Init(const NetParameter& in_param) {
 3   ........//过滤校验参数FilterNet
 4   FilterNet(in_param, &filtered_param);
 5   .........//插入Splits层
 6   InsertSplits(filtered_param, &param);
 7   .......// 构建网络中输入输出存储结构
 8   bottom_vecs_.resize(param.layer_size());
 9   top_vecs_.resize(param.layer_size());
10   bottom_id_vecs_.resize(param.layer_size());
11   param_id_vecs_.resize(param.layer_size());
12   top_id_vecs_.resize(param.layer_size());
13   bottom_need_backward_.resize(param.layer_size());
14 
15   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
16    ...//创建层
17  layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
18     layer_names_.push_back(layer_param.name());
19     LOG_IF(INFO, Caffe::root_solver())
20         << "Creating Layer " << layer_param.name();
21     bool need_backward = false;
22 
23     // Figure out this layer's input and output
24     for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
25          ++bottom_id) {
26       const int blob_id = AppendBottom(param, layer_id, bottom_id,
27                                        &available_blobs, &blob_name_to_idx);
28 
29 
30    ........//创建相关blob
31     // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter
32     // specified fewer than the required number (as specified by
33     // ExactNumTopBlobs() or MinTopBlobs()), allocate them here.
34     Layer<Dtype>* layer = layers_[layer_id].get();
35     if (layer->AutoTopBlobs()) {
36       const int needed_num_top =
37           std::max(layer->MinTopBlobs(), layer->ExactNumTopBlobs());
38       for (; num_top < needed_num_top; ++num_top) {
39         // Add "anonymous" top blobs -- do not modify available_blobs or
40         // blob_name_to_idx as we don't want these blobs to be usable as input
41         // to other layers.
42         AppendTop(param, layer_id, num_top, NULL, NULL);
43       }
44     }
45 
46 
47     .....//执行SetUp()
48     // After this layer is connected, set it up.
49     layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
50     LOG_IF(INFO, Caffe::root_solver())
51         << "Setting up " << layer_names_[layer_id];
52     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
53       if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
54         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
55       }
56       blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
57       LOG_IF(INFO, Caffe::root_solver())
58           << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
59       if (layer->loss(top_id)) {
60         LOG_IF(INFO, Caffe::root_solver())
61             << "    with loss weight " << layer->loss(top_id);
62       }
63       memory_used_ += top_vecs_[layer_id][top_id]->count();
64     }
65     LOG_IF(INFO, Caffe::root_solver())
66         << "Memory required for data: " << memory_used_ * sizeof(Dtype);
67     const int param_size = layer_param.param_size();
68     const int num_param_blobs = layers_[layer_id]->blobs().size();
69     CHECK_LE(param_size, num_param_blobs)
70         << "Too many params specified for layer " <<

Net::Init()

SetUp是怎么构建的呢？

 1 virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 2       const vector<Blob<Dtype>*>& top) {}
 3 
 4  void SetUp(const vector<Blob<Dtype>*>& bottom,
 5       const vector<Blob<Dtype>*>& top) {
 6     InitMutex();
 7     CheckBlobCounts(bottom, top);
 8     LayerSetUp(bottom, top);
 9     Reshape(bottom, top);
10     SetLossWeights(top);
11   }

初始化的总体流程大概就是新建一个Solver对象，然后调用Solver类的构造函数，然后在Solver的构造函数中又会新建Net类实例，在Net类的构造函数中又会新建各个layer的实例，一直具体到设置每个Blob，大概就完成了网络初始化的工作了。

Solve

train函数中CreateSolver()执行完成后，接下来是具体训练过程，执行Solve()函数---->Step()--->结束

Solve的具体内容和代码：

 1 template <typename Dtype>
 2 void Solver<Dtype>::Solve(const char* resume_file) {
 3   CHECK(Caffe::root_solver());
 4   LOG(INFO) << "Solving " << net_->name();
 5   LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
 6 
 7   // For a network that is trained by the solver, no bottom or top vecs
 8   // should be given, and we will just provide dummy vecs.
 9   int start_iter = iter_;
10   Step(param_.max_iter() - iter_);
11   
12   // overridden by setting snapshot_after_train := false
13   if (param_.snapshot_after_train()
14       && (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
15     Snapshot();
16   }
17  
18   // display loss
19   if (param_.display() && iter_ % param_.display() == 0) {
20     int average_loss = this->param_.average_loss();
21     Dtype loss;
22     net_->Forward(&loss);
23 
24     UpdateSmoothedLoss(loss, start_iter, average_loss);
25 
26     
27   if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
28     TestAll();
29   }
30 }

然后开始执行Step函数，具体内容和代码：

 1 template <typename Dtype>  
 2 void Solver<Dtype>::Step(int iters)  
 3 {  
 4     // 起始迭代步数  
 5     const int start_iter = iter_;  
 6     // 终止迭代步数  
 7     const int stop_iter = iter_ + iters;  
 8 
 9     // 判断是否已经完成设定步数  
10     while (iter_ < stop_iter)  
11     {  
12         // 将net_中的Bolb梯度参数置为零  
13         net_->ClearParamDiffs();  
14 
15         ...  
16 
17         // accumulate the loss and gradient  
18         Dtype loss = 0;  
19         for (int i = 0; i < param_.iter_size(); ++i)  
20         {  
21             // 正向传导和反向传导，并计算loss  
22             loss += net_->ForwardBackward();  
23         }  
24         loss /= param_.iter_size();  
25 
26         // 为了输出结果平滑，将临近的average_loss个loss数值进行平均，存储在成员变量smoothed_loss_中  
27         UpdateSmoothedLoss(loss, start_iter, average_loss);  
28 
29         // BP算法更新权重  
30         ApplyUpdate();  
31 
32         // Increment the internal iter_ counter -- its value should always indicate  
33         // the number of times the weights have been updated.  
34         ++iter_;  
35     }  
36 }

while循环中先调用了网络类Net::ForwardBackward()成员函数进行正向传导和反向传导，并计算loss

1 Dtype ForwardBackward() {
2     Dtype loss;
3     //正向传导
4     Forward(&loss);
5     //反向传导
6     Backward();
7     return loss;
8   }

而Fordward函数中调用了ForwardFromTo，而FordwardFromTo又调用了每个layer的Fordward。反向传导函数Backward()调用了BackwardFromTo(int start, int end)函数。正向传导和反向传导结束后，再调用SGDSolver::ApplyUpdate()成员函数进行权重更新。

ForwardBackward：按顺序调用了Forward和Backward。
ForwardFromTo(int start, int end)：执行从start层到end层的前向传递，采用简单的for循环调用。，forward只要计算损失loss
BackwardFromTo(int start, int end)：和前面的ForwardFromTo函数类似，调用从start层到end层的反向传递。backward主要根据loss来计算梯度，caffe通过自动求导并反向组合每一层的梯度来计算整个网络的梯度。
ToProto函数完成网络的序列化到文件，循环调用了每个层的ToProto函数

 1 template <typename Dtype>  
 2 void SGDSolver<Dtype>::ApplyUpdate()  
 3 {  
 4     // 获取当前学习速率  
 5     Dtype rate = GetLearningRate();  
 6     if (this->param_.display() && this->iter_ % this->param_.display() == 0)  
 7     {  
 8         LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;  
 9     }  
10 
11     // 在计算当前梯度的时候，如果该值超过了阈值clip_gradients，则将梯度直接设置为该阈值  
12     // 此处阈值设为-1，即不起作用  
13     ClipGradients();  
14 
15     // 逐层更新网络中的可学习层  
16     for (int param_id = 0; param_id < this->net_->learnable_params().size();  
17        ++param_id)  
18     {  
19         // 归一化  
20         Normalize(param_id);  
21         // L2范数正则化添加衰减权重  
22         Regularize(param_id);  
23         // 随机梯度下降法计算更新值  
24         ComputeUpdateValue(param_id, rate);  
25     }  
26     // 更新权重  
27     this->net_->Update();  
28 }