Prediction: implement records_to_data_for_learning

modules/prediction/common/feature_output.cc

@@ -16,7 +16,6 @@
 
 #include "modules/prediction/common/feature_output.h"
 
-#include <string>
 #include <vector>
 
 #include "modules/common/util/file.h"
@@ -59,7 +58,7 @@ void FeatureOutput::Insert(const Feature& feature) {
 
 void FeatureOutput::InsertDataForLearning(
     const Feature& feature, const std::vector<double>& feature_values,
-    const std::vector<double>& labels) {
+    const std::string& category) {
   DataForLearning* data_for_learning =
       list_data_for_learning_.add_data_for_learning();
   data_for_learning->set_id(feature.id());
@@ -67,9 +66,7 @@ void FeatureOutput::InsertDataForLearning(
   for (size_t i = 0; i < feature_values.size(); ++i) {
     data_for_learning->add_features_for_learning(feature_values[i]);
   }
-  for (size_t i = 0; i < labels.size(); ++i) {
-    data_for_learning->add_labels(labels[i]);
-  }
+  data_for_learning->set_category(category);
 }
 
 void FeatureOutput::Write() {

modules/prediction/common/feature_output.h

@@ -17,6 +17,7 @@
 #pragma once
 
 #include <vector>
+#include <string>
 
 #include "modules/prediction/proto/offline_features.pb.h"
 
@@ -58,7 +59,7 @@ class FeatureOutput {
     */
   static void InsertDataForLearning(
       const Feature& feature, const std::vector<double>& feature_values,
-      const std::vector<double>& labels);
+      const std::string& category);
 
   /**
    * @brief Write features to a file

modules/prediction/evaluator/vehicle/lane_scanning_evaluator.cc

@@ -74,7 +74,7 @@ void LaneScanningEvaluator::Evaluate(
   std::vector<double> labels = {0.0};
   if (FLAGS_prediction_offline_dataforlearning) {
     FeatureOutput::InsertDataForLearning(*latest_feature_ptr, feature_values,
-                                         labels);
+                                         "cruise");
     ADEBUG << "Save extracted features for learning locally.";
   } else {
     // TODO(jiacheng): once the model is trained, implement this online part.

modules/prediction/pipeline/BUILD

@@ -29,4 +29,12 @@ cc_binary(
     ],
 )
 
+cc_binary(
+   name = "records_to_data_for_learning",
+    srcs = ["records_to_data_for_learning.cc"],
+    deps = [
+        "//modules/prediction/common:message_process",
+    ],
+)
+
 cpplint()

modules/prediction/pipeline/records_to_data_for_learning.cc

+/******************************************************************************
+ * Copyright 2019 The Apollo Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *****************************************************************************/
+
+#include "cyber/common/file.h"
+
+#include "modules/common/util/string_util.h"
+#include "modules/prediction/common/feature_output.h"
+#include "modules/prediction/common/message_process.h"
+#include "modules/prediction/common/prediction_system_gflags.h"
+#include "modules/prediction/util/data_extraction.h"
+
+namespace apollo {
+namespace prediction {
+
+void GenerateDataForLearning() {
+  if (!FeatureOutput::Ready()) {
+    AERROR << "Feature output is not ready.";
+    return;
+  }
+  if (FLAGS_prediction_offline_bags.empty()) {
+    return;
+  }
+  std::vector<std::string> inputs;
+  common::util::Split(FLAGS_prediction_offline_bags, ':', &inputs);
+  for (const auto& input : inputs) {
+    std::vector<std::string> offline_bags;
+    GetRecordFileNames(boost::filesystem::path(input), &offline_bags);
+    std::sort(offline_bags.begin(), offline_bags.end());
+    AINFO << "For input " << input << ", found " << offline_bags.size()
+          << "  rosbags to process";
+    for (std::size_t i = 0; i < offline_bags.size(); ++i) {
+      AINFO << "\tProcessing: [ " << i << " / " << offline_bags.size()
+            << " ]: " << offline_bags[i];
+      MessageProcess::ProcessOfflineData(offline_bags[i]);
+    }
+  }
+  FeatureOutput::Close();
+}
+
+}  // namespace prediction
+}  // namespace apollo
+
+int main(int argc, char *argv[]) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  apollo::prediction::GenerateDataForLearning();
+  return 0;
+}

modules/prediction/proto/feature.proto

@@ -3,7 +3,6 @@ syntax = "proto2";
 package apollo.prediction;
 
 import "modules/common/proto/geometry.proto";
-import "modules/common/proto/pnc_point.proto";
 import "modules/perception/proto/perception_obstacle.proto";
 import "modules/prediction/proto/lane_graph.proto";
 import "modules/prediction/proto/prediction_point.proto";