（二）caffe 网络训练执行流程

暂时提交
caffe训练网络的执行脚本如下（例如运行lenet）

./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototx

在Qt中运行的时候，参考http://blog.csdn.net/qyczyr/article/details/70216300

程序执行入口啊

在src/caffe/tools/caffe.cpp 包含了程序的入口函数main 。

#ifndef CAFFE_SOLVER_HPP_#define CAFFE_SOLVER_HPP_#include <boost/function.hpp>#include <string>#include <vector>#include "caffe/net.hpp"#include "caffe/solver_factory.hpp"namespace caffe {  namespace SolverAction {    enum Enum {      NONE = 0,  // Take no special action.      STOP = 1,  // Stop training. snapshot_after_train controls whether a                 // snapshot is created.      SNAPSHOT = 2  // Take a snapshot, and keep training.    };  }/** * @brief Type of a function that returns a Solver Action enumeration. */typedef boost::function<SolverAction::Enum()> ActionCallback;/** * @brief An interface for classes that perform optimization on Net%s. * * Requires implementation of ApplyUpdate to compute a parameter update * given the current state of the Net parameters. */template <typename Dtype>class Solver { public:  explicit Solver(const SolverParameter& param,      const Solver* root_solver = NULL);  explicit Solver(const string& param_file, const Solver* root_solver = NULL);  void Init(const SolverParameter& param);  void InitTrainNet();  void InitTestNets();  // Client of the Solver optionally may call this in order to set the function  // that the solver uses to see what action it should take (e.g. snapshot or  // exit training early).  void SetActionFunction(ActionCallback func);  SolverAction::Enum GetRequestedAction();  // The main entry of the solver function. In default, iter will be zero. Pass  // in a non-zero iter number to resume training for a pre-trained net.  virtual void Solve(const char* resume_file = NULL);  inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }  void Step(int iters);  // The Restore method simply dispatches to one of the  // RestoreSolverStateFrom___ protected methods. You should implement these  // methods to restore the state from the appropriate snapshot type.  void Restore(const char* resume_file);  // The Solver::Snapshot function implements the basic snapshotting utility  // that stores the learned net. You should implement the SnapshotSolverState()  // function that produces a SolverState protocol buffer that needs to be  // written to disk together with the learned net.  void Snapshot();  virtual ~Solver() {}  inline const SolverParameter& param() const { return param_; }  inline shared_ptr<Net<Dtype> > net() { return net_; }  inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {    return test_nets_;  }  int iter() { return iter_; }  // Invoked at specific points during an iteration  class Callback {   protected:    virtual void on_start() = 0;    virtual void on_gradients_ready() = 0;    template <typename T>    friend class Solver;  };  const vector<Callback*>& callbacks() const { return callbacks_; }  void add_callback(Callback* value) {    callbacks_.push_back(value);  }  void CheckSnapshotWritePermissions();  /**   * @brief Returns the solver type.   */  virtual inline const char* type() const { return ""; } protected:  // Make and apply the update value for the current iteration.  virtual void ApplyUpdate() = 0;  string SnapshotFilename(const string extension);  string SnapshotToBinaryProto();  string SnapshotToHDF5();  // The test routine  void TestAll();  void Test(const int test_net_id = 0);  virtual void SnapshotSolverState(const string& model_filename) = 0;  virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0;  virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;  void DisplayOutputBlobs(const int net_id);  void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss);  SolverParameter param_;  int iter_;  int current_step_;  shared_ptr<Net<Dtype> > net_;  vector<shared_ptr<Net<Dtype> > > test_nets_;  vector<Callback*> callbacks_;  vector<Dtype> losses_;  Dtype smoothed_loss_;  // The root solver that holds root nets (actually containing shared layers)  // in data parallelism  const Solver* const root_solver_;  // A function that can be set by a client of the Solver to provide indication  // that it wants a snapshot saved and/or to exit early.  ActionCallback action_request_function_;  // True iff a request to stop early was received.  bool requested_early_exit_;  DISABLE_COPY_AND_ASSIGN(Solver);};/** * @brief Solver that only computes gradients, used as worker *        for multi-GPU training. */template <typename Dtype>class WorkerSolver : public Solver<Dtype> { public:  explicit WorkerSolver(const SolverParameter& param,      const Solver<Dtype>* root_solver = NULL)      : Solver<Dtype>(param, root_solver) {} protected:  void ApplyUpdate() {}  void SnapshotSolverState(const string& model_filename) {    LOG(FATAL) << "Should not be called on worker solver.";  }  void RestoreSolverStateFromBinaryProto(const string& state_file) {    LOG(FATAL) << "Should not be called on worker solver.";  }  void RestoreSolverStateFromHDF5(const string& state_file) {    LOG(FATAL) << "Should not be called on worker solver.";  }};}  // namespace caffe#endif  // CAFFE_SOLVER_HPP_