void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
const string& model_filename) {
SolverState state;
state.set_iter(this->iter_);
state.set_learned_net(model_filename);
state.set_current_step(this->current_step_);
state.clear_history();
for (int i = 0; i < history_.size(); ++i) {
// Add history
BlobProto* history_blob = state.add_history();
history_[i]->ToProto(history_blob);
}
string snapshot_filename = Solver<Dtype>::SnapshotFilename(".solverstate");
LOG(INFO)
<< "Snapshotting solver state to binary proto file " << snapshot_filename;
WriteProtoToBinaryFile(state, snapshot_filename.c_str());
}
void Solver<Dtype>::Snapshot() {
NetParameter net_param;
// For intermediate results, we will also dump the gradient values.
net_->ToProto(&net_param, param_.snapshot_diff());
string filename(param_.snapshot_prefix());
string model_filename, snapshot_filename;
const int kBufferSize = 20;
char iter_str_buffer[kBufferSize];
// Add one to iter_ to get the number of iterations that have completed.
snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_ + 1);
filename += iter_str_buffer;
model_filename = filename + ".caffemodel";
LOG(INFO) << "Snapshotting to " << model_filename;
WriteProtoToBinaryFile(net_param, model_filename.c_str());
SolverState state;
SnapshotSolverState(&state);
state.set_iter(iter_ + 1);
state.set_learned_net(model_filename);
state.set_current_step(current_step_);
snapshot_filename = filename + ".solverstate";
LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
WriteProtoToBinaryFile(state, snapshot_filename.c_str());
}