Solver.hpp Solver.cpp学习
主要实现了一个模板类solver,而且是个抽象类。
SolverParameter param_;
int iter_;//在测试的时候，需要迭代的次数，即test_iter* batchsize（测试集的）=测试集的大小，测试集batchsize可以在prototxt文件里设置
int current_step_;
shared_ptr > net_;
vector > > test_nets_;//test net可以有多个
vector callbacks_;//嵌套类，暂时还不知道它的作用
// 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_; 然后看一下主要的几个成员函数
===========================构造函数==========================================
会调用Init()方法进行初始化，即Solver scaffolding
template
Solver::Solver(const SolverParameter& param, const Solver* root_solver)
: net_(), callbacks_(), root_solver_(root_solver),
requested_early_exit_(false) {
Init(param);
}
===========================Init()方法========================================
会调用InitTrainNet()和InitTestNet()来初始化TrainNet、TestNet
template
void Solver::Init(const SolverParameter& param) {
CHECK(Caffe::root_solver() || root_solver_)
<< "root_solver_ needs to be set for all non-root solvers";
LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "
<< std::endl << param.DebugString();
param_ = param;//为solver类的数据成员param_赋值
CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
CheckSnapshotWritePermissions();
if (Caffe::root_solver() && param_.random_seed() >= 0) {
Caffe::set_random_seed(param_.random_seed());//调用Caffe命名空间里的set_random_seed函数，而不是caffe类的set_random_seed函数；param_.random_seed()实际上调用的是::google::protobuf::int64 random_seed()
}
// Scaffolding code
InitTrainNet();
if (Caffe::root_solver()) {
InitTestNets();
LOG(INFO) << "Solver scaffolding done.";
}
iter_ = 0;
current_step_ = 0;
} ===============================InitTrainNet()方法=========================================
template
void Solver::InitTrainNet() {
const int num_train_nets = param_.has_net() + param_.has_net_param() +
param_.has_train_net() + param_.has_train_net_param();
const string& field_names = "net, net_param, train_net, train_net_param";
//只能有一个train net
CHECK_GE(num_train_nets, 1) << "SolverParameter must specify a train net "
<< "using one of these fields: " << field_names;
CHECK_LE(num_train_nets, 1) << "SolverParameter must not contain more than "
<< "one of these fields specifying a train_net: " << field_names;
NetParameter net_param;
if (param_.has_train_net_param()) {
LOG_IF(INFO, Caffe::root_solver())
<< "Creating training net specified in train_net_param.";
net_param.CopyFrom(param_.train_net_param());
} else if (param_.has_train_net()) {
LOG_IF(INFO, Caffe::root_solver())
<< "Creating training net from train_net file: " << param_.train_net();
ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param);
}
if (param_.has_net_param()) {
LOG_IF(INFO, Caffe::root_solver())
<< "Creating training net specified in net_param.";
net_param.CopyFrom(param_.net_param());
}
if (param_.has_net()) {
LOG_IF(INFO, Caffe::root_solver())
<< "Creating training net from net file: " << param_.net();
ReadNetParamsFromTextFileOrDie(param_.net(), &net_param);
}
// Set the correct NetState. We start with the solver defaults (lowest
// precedence); then, merge in any NetState specified by the net_param itself;
// finally, merge in any NetState specified by the train_state (highest
// precedence).
NetState net_state;
net_state.set_phase(TRAIN);
net_state.MergeFrom(net_param.state());//从低到高获取state,最终从最高优先级SolverParameter类型中的train_state,显然这会覆盖掉之前获取的state。
net_state.MergeFrom(param_.train_state());//这里获取的state可以为Netparameter中的state赋值，然后可以根据LayerParameter中的include和exclude来确定该层是否应该包含在网络中。
net_param.mutable_state()->CopyFrom(net_state);//这是Initialize train net 的一部分工作。InitTestNets也是如此
if (Caffe::root_solver()) {
net_.reset(new Net(net_param));//调用模板类的构造函数，进行net的初始化
} else {
net_.reset(new Net(net_param, root_solver_->net_.get()));
}
} ===================================InitTestNet()方法=======================================
需要注意的是TestNet可以有多个，而TrainNet只能有一个
template
void Solver::InitTestNets() {
CHECK(Caffe::root_solver());
const bool has_net_param = param_.has_net_param();
const bool has_net_file = param_.has_net();
const int num_generic_nets = has_net_param + has_net_file;
CHECK_LE(num_generic_nets, 1)
<< "Both net_param and net_file may not be specified.";
const int num_test_net_params = param_.test_net_param_size();
const int num_test_net_files = param_.test_net_size();
const int num_test_nets = num_test_net_params + num_test_net_files;
if (num_generic_nets) {
CHECK_GE(param_.test_iter_size(), num_test_nets)
<< "test_iter must be specified for each test network.";
} else {
CHECK_EQ(param_.test_iter_size(), num_test_nets)
<< "test_iter must be specified for each test network.";
}
// If we have a generic net (specified by net or net_param, rather than
// test_net or test_net_param), we may have an unlimited number of actual
// test networks -- the actual number is given by the number of remaining
// test_iters after any test nets specified by test_net_param and/or test_net
// are evaluated.
// 可以有多个test net
const int num_generic_net_instances = param_.test_iter_size() - num_test_nets;
const int num_test_net_instances = num_test_nets + num_generic_net_instances;//num_test_net_instances由num_test_nets 和 num_generic_net_instances 组成，实际上也就是param_.test_iter_size()
if (param_.test_state_size()) {
CHECK_EQ(param_.test_state_size(), num_test_net_instances)
<< "test_state must be unspecified or specified once per test net.";
}
if (num_test_net_instances) {
CHECK_GT(param_.test_interval(), 0);
}
int test_net_id = 0;
vector sources(num_test_net_instances);
vector net_params(num_test_net_instances);
for (int i = 0; i < num_test_net_params; ++i, ++test_net_id) {
sources[test_net_id] = "test_net_param";
net_params[test_net_id].CopyFrom(param_.test_net_param(i));
}
for (int i = 0; i < num_test_net_files; ++i, ++test_net_id) {
sources[test_net_id] = "test_net file: " + param_.test_net(i);
ReadNetParamsFromTextFileOrDie(param_.test_net(i),
&net_params[test_net_id]);
}
const int remaining_test_nets = param_.test_iter_size() - test_net_id;
if (has_net_param) {
for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {
sources[test_net_id] = "net_param";
net_params[test_net_id].CopyFrom(param_.net_param());
}
}
if (has_net_file) {
for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {
sources[test_net_id] = "net file: " + param_.net();
ReadNetParamsFromTextFileOrDie(param_.net(), &net_params[test_net_id]);
}
}
test_nets_.resize(num_test_net_instances);
for (int i = 0; i < num_test_net_instances; ++i) {
// Set the correct NetState. We start with the solver defaults (lowest
// precedence); then, merge in any NetState specified by the net_param
// itself; finally, merge in any NetState specified by the test_state
// (highest precedence).
NetState net_state;
net_state.set_phase(TEST);
net_state.MergeFrom(net_params[i].state());
if (param_.test_state_size()) {
net_state.MergeFrom(param_.test_state(i));
}
net_params[i].mutable_state()->CopyFrom(net_state);
LOG(INFO)
<< "Creating test net (#" << i << ") specified by " << sources[i];
if (Caffe::root_solver()) {
test_nets_[i].reset(new Net(net_params[i]));
} else {
test_nets_[i].reset(new Net(net_params[i],
root_solver_->test_nets_[i].get()));
}
test_nets_[i]->set_debug_info(param_.debug_info());
}
}
=============================Step()方法============================
template
void Solver::Step(int iters) {
vector*> bottom_vec;
const int start_iter = iter_;
const int stop_iter = iter_ + iters;
int average_loss = this->param_.average_loss();
vector losses;
Dtype smoothed_loss = 0;
while (iter_ < stop_iter) {
// zero-init the params
net_->ClearParamDiffs();
//test_initialization默认为true
if (param_.test_interval() && iter_ % param_.test_interval() == 0
&& (iter_ > 0 || param_.test_initialization())
&& Caffe::root_solver()) {
TestAll();
if (requested_early_exit_) {
// Break out of the while loop because stop was requested while testing.
break;
}
}
for (int i = 0; i < callbacks_.size(); ++i) {
callbacks_[i]->on_start();
}
const bool display = param_.display() && iter_ % param_.display() == 0;
net_->set_debug_info(display && param_.debug_info());
// accumulate the loss and gradient
Dtype loss = 0;
for (int i = 0; i < param_.iter_size(); ++i) {
loss += net_->ForwardBackward(bottom_vec);
}
loss /= param_.iter_size();//accumulate（累积） gradients over `iter_size` x `batch_size` instances。默认情况下，iter_size=1,即默认情况下，一个iteratio一个batch
// average the loss across iterations for smoothed reporting.
// average_loss [default = 1]——> Display the loss averaged over the last average_loss iterations
if (losses.size() < average_loss) {
losses.push_back(loss);
int size = losses.size();
smoothed_loss = (smoothed_loss * (size - 1) + loss) / size;
} else {
int idx = (iter_ - start_iter) % average_loss;
smoothed_loss += (loss - losses[idx]) / average_loss;
losses[idx] = loss;
}
if (display) {
LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_
<< ", loss = " << smoothed_loss;
const vector*>& result = net_->output_blobs();
int score_index = 0;
for (int j = 0; j < result.size(); ++j) {
const Dtype* result_vec = result[j]->cpu_data();
const string& output_name =
net_->blob_names()[net_->output_blob_indices()[j]];
const Dtype loss_weight =
net_->blob_loss_weights()[net_->output_blob_indices()[j]];
for (int k = 0; k < result[j]->count(); ++k) {
ostringstream loss_msg_stream;
if (loss_weight) {
loss_msg_stream << " (* " << loss_weight
<< " = " << loss_weight * result_vec[k] << " loss)";
}
LOG_IF(INFO, Caffe::root_solver()) << " Train net output #"
<< score_index++ << ": " << output_name << " = "
<< result_vec[k] << loss_msg_stream.str();
}
}
}
for (int i = 0; i < callbacks_.size(); ++i) {
callbacks_[i]->on_gradients_ready();
}
ApplyUpdate();
// Increment the internal iter_ counter -- its value should always indicate
// the number of times the weights have been updated.
++iter_;
SolverAction::Enum request = GetRequestedAction();
// Save a snapshot if needed.
if ((param_.snapshot()
&& iter_ % param_.snapshot() == 0
&& Caffe::root_solver()) ||
(request == SolverAction::SNAPSHOT)) {
Snapshot();
}
if (SolverAction::STOP == request) {
requested_early_exit_ = true;
// Break out of training loop.
break;
}
}
}
=================================Test()方法==============================
template
void Solver::Test(const int test_net_id) {
CHECK(Caffe::root_solver());
LOG(INFO) << "Iteration " << iter_
<< ", Testing net (#" << test_net_id << ")";
//检查是否有layer共享于多个网络
CHECK_NOTNULL(test_nets_[test_net_id].get())->
ShareTrainedLayersWith(net_.get());
vector test_score;
vector test_score_output_id;
vector*> bottom_vec;
const shared_ptr >& test_net = test_nets_[test_net_id];
Dtype loss = 0;
for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
SolverAction::Enum request = GetRequestedAction();
// Check to see if stoppage of testing/training has been requested.
while (request != SolverAction::NONE) {
if (SolverAction::SNAPSHOT == request) {
Snapshot();
} else if (SolverAction::STOP == request) {
requested_early_exit_ = true;
}
request = GetRequestedAction();
}
if (requested_early_exit_) {
// break out of test loop.
break;
}
Dtype iter_loss;
const vector*>& result =
test_net->Forward(bottom_vec, &iter_loss);
if (param_.test_compute_loss()) {
loss += iter_loss;
}
if (i == 0) {
for (int j = 0; j < result.size(); ++j) {
const Dtype* result_vec = result[j]->cpu_data();
for (int k = 0; k < result[j]->count(); ++k) {
test_score.push_back(result_vec[k]);
test_score_output_id.push_back(j);
}
}
} else {
int idx = 0;
for (int j = 0; j < result.size(); ++j) {
const Dtype* result_vec = result[j]->cpu_data();
for (int k = 0; k < result[j]->count(); ++k) {
test_score[idx++] += result_vec[k];
}
}
}
}
if (requested_early_exit_) {
LOG(INFO) << "Test interrupted.";
return;
}
if (param_.test_compute_loss()) {
loss /= param_.test_iter(test_net_id);
LOG(INFO) << "Test loss: " << loss;
}
for (int i = 0; i < test_score.size(); ++i) {
const int output_blob_index =
test_net->output_blob_indices()[test_score_output_id[i]];
const string& output_name = test_net->blob_names()[output_blob_index];
const Dtype loss_weight = test_net->blob_loss_weights()[output_blob_index];
ostringstream loss_msg_stream;
const Dtype mean_score = test_score[i] / param_.test_iter(test_net_id);//求多次迭代Loss的平均值，也就是求多个batch的平局值，因为一次迭代用的是一个test batch-size 的图片
if (loss_weight) {
loss_msg_stream << " (* " << loss_weight
<< " = " << loss_weight * mean_score << " loss)";
}
LOG(INFO) << " Test net output #" << i << ": " << output_name << " = "
<< mean_score << loss_msg_stream.str();
}
} =======================================Solve()方法：===================================================
对整个网络进行训练（也就是你运行Caffe训练某个模型）的时候，实际上是在运行caffe.cpp中的train( )函数，而这个函数实际上是实例化一个Solver对象，初始化后调用了Solver中的Solve( )方法
调用此方法训练网络，其中会调用Step()方法来迭代，迭代 param_.max_iter() - iter_ 次
template
void Solver::Solve(const char* resume_file) {
CHECK(Caffe::root_solver());
LOG(INFO) << "Solving " << net_->name();
LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
// Initialize to false every time we start solving.
requested_early_exit_ = false;
if (resume_file) {
LOG(INFO) << "Restoring previous solver status from " << resume_file;
Restore(resume_file);
}
// For a network that is trained by the solver, no bottom or top vecs
// should be given, and we will just provide dummy vecs.
Step(param_.max_iter() - iter_);
// If we haven't already, save a snapshot after optimization, unless
// overridden by setting snapshot_after_train := false
if (param_.snapshot_after_train()
&& (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
Snapshot();
}
if (requested_early_exit_) {
LOG(INFO) << "Optimization stopped early.";
return;
}
// After the optimization is done, run an additional train and test pass to
// display the train and test loss/outputs if appropriate (based on the
// display and test_interval settings, respectively). Unlike in the rest of
// training, for the train net we only run a forward pass as we've already
// updated the parameters "max_iter" times -- this final pass is only done to
// display the loss, which is computed in the forward pass.
if (param_.display() && iter_ % param_.display() == 0) {
Dtype loss;
net_->ForwardPrefilled(&loss);
LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
}
if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
TestAll();
}
LOG(INFO) << "Optimization Done.";
} Snapshot()输出当前网络状态到一个文件中。
Restore()从一个文件中读入网络状态，并可以从那个状态恢复。参考文献：http://blog.csdn.net/qq_16055159/article/details/45068147