planning: update learning data proto to align with the layout better

modules/planning/common/feature_output.cc

@@ -41,18 +41,19 @@ bool FeatureOutput::Ready() {
 void FeatureOutput::InsertLearningDataFrame(
     const std::string& record_file,
     const LearningDataFrame& learning_data_frame) {
-  learning_data_.add_learning_data()->CopyFrom(learning_data_frame);
+  learning_data_.add_learning_data_frame()->CopyFrom(learning_data_frame);
 
   // write frames into a file
-  if (learning_data_.learning_data_size() >=
+  if (learning_data_.learning_data_frame_size() >=
       FLAGS_learning_data_frame_num_per_file) {
     WriteLearningData(record_file);
   }
 }
 
 LearningDataFrame* FeatureOutput::GetLatestLearningDataFrame() {
-  const int size = learning_data_.learning_data_size();
-  return size > 0 ? learning_data_.mutable_learning_data(size - 1) : nullptr;
+  const int size = learning_data_.learning_data_frame_size();
+  return size > 0 ? learning_data_.mutable_learning_data_frame(size - 1)
+      : nullptr;
 }
 
 void FeatureOutput::InsertPlanningResult() {}
@@ -72,7 +73,7 @@ void FeatureOutput::WriteLearningData(const std::string& record_file) {
 
 void FeatureOutput::WriteRemainderiLearningData(
     const std::string& record_file) {
-  if (learning_data_.learning_data_size() > 0) {
+  if (learning_data_.learning_data_frame_size() > 0) {
     WriteLearningData(record_file);
   }
 }

modules/planning/common/learning_based_data.cc

@@ -27,16 +27,17 @@ void LearningBasedData::Clear() {
 
 void LearningBasedData::InsertLearningDataFrame(
     const LearningDataFrame& learning_data_frame) {
-  learning_data_.add_learning_data()->CopyFrom(learning_data_frame);
+  learning_data_.add_learning_data_frame()->CopyFrom(learning_data_frame);
 
-  while (learning_data_.learning_data_size() > 10) {
-    learning_data_.mutable_learning_data()->DeleteSubrange(0, 1);
+  while (learning_data_.learning_data_frame_size() > 10) {
+    learning_data_.mutable_learning_data_frame()->DeleteSubrange(0, 1);
   }
 }
 
 LearningDataFrame* LearningBasedData::GetLatestLearningDataFrame() {
-  const int size = learning_data_.learning_data_size();
-  return size > 0 ? learning_data_.mutable_learning_data(size - 1) : nullptr;
+  const int size = learning_data_.learning_data_frame_size();
+  return size > 0 ? learning_data_.mutable_learning_data_frame(size - 1)
+      : nullptr;
 }
 
 }  // namespace planning

modules/planning/pipeline/evaluator.cc

@@ -54,8 +54,8 @@ void Evaluator::Evaluate(const std::string& source_file) {
 
   cyber::common::GetProtoFromFile(source_file, &learning_data_);
 
-  for (int i = 0; i < learning_data_.learning_data_size(); ++i) {
-    auto learning_data_frame = learning_data_.mutable_learning_data(i);
+  for (int i = 0; i < learning_data_.learning_data_frame_size(); ++i) {
+    auto learning_data_frame = learning_data_.mutable_learning_data_frame(i);
     if (learning_data_frame->adc_trajectory_point_size() <= 0) {
       continue;
     }
@@ -72,14 +72,15 @@ void Evaluator::Evaluate(const std::string& source_file) {
 
     // evaluate adc future trajectory
     std::vector<TrajectoryPointFeature> adc_future_trajectory;
-    for (const auto& tp : learning_data_.learning_data(i)
+    for (const auto& tp : learning_data_.learning_data_frame(i)
                               .output()
                               .adc_future_trajectory_point()) {
       adc_future_trajectory.push_back(tp);
     }
     std::vector<TrajectoryPointFeature> evaluated_adc_future_trajectory;
     trajectory_evaluator_.EvaluateADCFutureTrajectory(
-        learning_data_.learning_data(i).frame_num(), adc_future_trajectory,
+        learning_data_.learning_data_frame(i).frame_num(),
+        adc_future_trajectory,
         start_point_timestamp_sec, FLAGS_trajectory_delta_t,
         &evaluated_adc_future_trajectory);
     learning_data_frame->mutable_output()->clear_adc_future_trajectory_point();

modules/planning/planning_component.cc

@@ -175,7 +175,7 @@ bool PlanningComponent::Proc(
         injector_->learning_based_data()->GetLatestLearningDataFrame();
     if (learning_data_frame) {
       planning_learning_data.mutable_learning_data()
-                            ->add_learning_data()
+                            ->add_learning_data_frame()
                             ->CopyFrom(*learning_data_frame);
       common::util::FillHeader(node_->Name(), &planning_learning_data);
       planning_learning_data_writer_->Write(planning_learning_data);

modules/planning/proto/learning_data.proto

@@ -229,7 +229,7 @@ message LearningDataFrame {
 }
 
 message LearningData {
-  repeated LearningDataFrame learning_data = 1;
+  repeated LearningDataFrame learning_data_frame = 1;
 }
 
 message PlanningLearningData {