fix(lidar_centerpoint): non-maximum suppression target decision logic (…

…#9595) * refactor(lidar_centerpoint): optimize non-maximum suppression search distance calculation Signed-off-by: Taekjin LEE <[email protected]> * feat(lidar_centerpoint): do not suppress if one side of the object is pedestrian Signed-off-by: Taekjin LEE <[email protected]> * style(pre-commit): autofix * refactor(lidar_centerpoint): remove unused variables Signed-off-by: Taekjin LEE <[email protected]> * refactor: remove unused variables Signed-off-by: Taekjin LEE <[email protected]> fix: implement non-maximum suppression logic to the transfusion Signed-off-by: Taekjin LEE <[email protected]> refactor: remove unused parameter iou_nms_target_class_names Signed-off-by: Taekjin LEE <[email protected]> Revert "fix: implement non-maximum suppression logic to the transfusion" This reverts commit b8017fc. fix: revert transfusion modification --------- Signed-off-by: Taekjin LEE <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
autowarefoundation · Dec 11, 2024 · 01944ef · 01944ef
1 parent 386d2af
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diff --git a/perception/autoware_image_projection_based_fusion/config/pointpainting.param.yaml b/perception/autoware_image_projection_based_fusion/config/pointpainting.param.yaml
@@ -9,7 +9,6 @@
     post_process_params:
       # post-process params
       circle_nms_dist_threshold: 0.3
-      iou_nms_target_class_names: ["CAR"]
       iou_nms_search_distance_2d: 10.0
       iou_nms_threshold: 0.1
       score_threshold: 0.35

diff --git a/perception/autoware_image_projection_based_fusion/docs/pointpainting-fusion.md b/perception/autoware_image_projection_based_fusion/docs/pointpainting-fusion.md
@@ -43,7 +43,6 @@ The lidar points are projected onto the output of an image-only 2d object detect
 | `trt_precision`                                  | string       | `fp16`                    | TensorRT inference precision: `fp32` or `fp16`              |
 | `post_process_params.score_threshold`            | double       | `0.4`                     | detected objects with score less than threshold are ignored |
 | `post_process_params.yaw_norm_thresholds`        | list[double] | [0.3, 0.3, 0.3, 0.3, 0.0] | An array of distance threshold values of norm of yaw [rad]. |
-| `post_process_params.iou_nms_target_class_names` | list[string] | ["CAR"]                   | An array of class names to be target in NMS.                |
 | `post_process_params.iou_nms_search_distance_2d` | double       | 10.0                      | A maximum distance value to search the nearest objects.     |
 | `post_process_params.iou_nms_threshold`          | double       | 0.1                       | A threshold value of NMS using IoU score.                   |
 | `post_process_params.has_twist`                  | boolean      | false                     | Indicates whether the model outputs twist value.            |

diff --git a/perception/autoware_image_projection_based_fusion/schema/pointpainting.schema.json b/perception/autoware_image_projection_based_fusion/schema/pointpainting.schema.json
@@ -46,12 +46,6 @@
               "minimum": 0.0,
               "maximum": 1.0
             },
-            "iou_nms_target_class_names": {
-              "type": "array",
-              "description": "An array of class names to be target in NMS.",
-              "default": ["CAR"],
-              "uniqueItems": true
-            },
             "iou_nms_search_distance_2d": {
               "type": "number",
               "description": "A maximum distance value to search the nearest objects.",

diff --git a/perception/autoware_image_projection_based_fusion/src/pointpainting_fusion/node.cpp b/perception/autoware_image_projection_based_fusion/src/pointpainting_fusion/node.cpp
@@ -171,9 +171,6 @@ PointPaintingFusionNode::PointPaintingFusionNode(const rclcpp::NodeOptions & opt
 
   {
     autoware::lidar_centerpoint::NMSParams p;
-    p.nms_type_ = autoware::lidar_centerpoint::NMS_TYPE::IoU_BEV;
-    p.target_class_names_ = this->declare_parameter<std::vector<std::string>>(
-      "post_process_params.iou_nms_target_class_names");
     p.search_distance_2d_ =
       this->declare_parameter<double>("post_process_params.iou_nms_search_distance_2d");
     p.iou_threshold_ = this->declare_parameter<double>("post_process_params.iou_nms_threshold");

diff --git a/perception/autoware_lidar_centerpoint/README.md b/perception/autoware_lidar_centerpoint/README.md
@@ -56,7 +56,6 @@ Note that these parameters are associated with ONNX file, predefined during the
 | `trt_precision`                                  | string       | `fp16`                    | TensorRT inference precision: `fp32` or `fp16`                |
 | `post_process_params.score_threshold`            | double       | `0.4`                     | detected objects with score less than threshold are ignored   |
 | `post_process_params.yaw_norm_thresholds`        | list[double] | [0.3, 0.3, 0.3, 0.3, 0.0] | An array of distance threshold values of norm of yaw [rad].   |
-| `post_process_params.iou_nms_target_class_names` | list[string] | -                         | target classes for IoU-based Non Maximum Suppression          |
 | `post_process_params.iou_nms_search_distance_2d` | double       | -                         | If two objects are farther than the value, NMS isn't applied. |
 | `post_process_params.iou_nms_threshold`          | double       | -                         | IoU threshold for the IoU-based Non Maximum Suppression       |
 | `post_process_params.has_twist`                  | boolean      | false                     | Indicates whether the model outputs twist value.              |
@@ -267,7 +266,6 @@ point_cloud_range, point_feature_size, voxel_size, etc. according to the trainin
     encoder_in_feature_size: 9
     # post-process params
     circle_nms_dist_threshold: 0.5
-    iou_nms_target_class_names: ["CAR"]
     iou_nms_search_distance_2d: 10.0
     iou_nms_threshold: 0.1
     yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]

diff --git a/perception/autoware_lidar_centerpoint/config/centerpoint.param.yaml b/perception/autoware_lidar_centerpoint/config/centerpoint.param.yaml
@@ -10,7 +10,6 @@
     post_process_params:
       # post-process params
       circle_nms_dist_threshold: 0.5
-      iou_nms_target_class_names: ["CAR"]
       iou_nms_search_distance_2d: 10.0
       iou_nms_threshold: 0.1
       score_threshold: 0.35

diff --git a/perception/autoware_lidar_centerpoint/config/centerpoint_sigma.param.yaml b/perception/autoware_lidar_centerpoint/config/centerpoint_sigma.param.yaml
@@ -11,7 +11,6 @@
     post_process_params:
       # post-process params
       circle_nms_dist_threshold: 0.5
-      iou_nms_target_class_names: ["CAR"]
       iou_nms_search_distance_2d: 10.0
       iou_nms_threshold: 0.1
       yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]

diff --git a/perception/autoware_lidar_centerpoint/config/centerpoint_tiny.param.yaml b/perception/autoware_lidar_centerpoint/config/centerpoint_tiny.param.yaml
@@ -10,7 +10,6 @@
     post_process_params:
       # post-process params
       circle_nms_dist_threshold: 0.5
-      iou_nms_target_class_names: ["CAR"]
       iou_nms_search_distance_2d: 10.0
       iou_nms_threshold: 0.1
       score_threshold: 0.35

diff --git a/...ar_centerpoint/include/autoware/lidar_centerpoint/postprocess/non_maximum_suppression.hpp b/...ar_centerpoint/include/autoware/lidar_centerpoint/postprocess/non_maximum_suppression.hpp
@@ -28,36 +28,12 @@ namespace autoware::lidar_centerpoint
 {
 using autoware_perception_msgs::msg::DetectedObject;
 
-// TODO(yukke42): now only support IoU_BEV
-enum class NMS_TYPE {
-  IoU_BEV
-  // IoU_3D
-  // Distance_2D
-  // Distance_3D
-};
-
 struct NMSParams
 {
-  NMS_TYPE nms_type_{};
-  std::vector<std::string> target_class_names_{};
   double search_distance_2d_{};
   double iou_threshold_{};
-  // double distance_threshold_{};
 };
 
-std::vector<bool> classNamesToBooleanMask(const std::vector<std::string> & class_names)
-{
-  std::vector<bool> mask;
-  constexpr std::size_t num_object_classification = 8;
-  mask.resize(num_object_classification);
-  for (const auto & class_name : class_names) {
-    const auto semantic_type = getSemanticType(class_name);
-    mask.at(semantic_type) = true;
-  }
-
-  return mask;
-}
-
 class NonMaximumSuppression
 {
 public:
@@ -66,14 +42,13 @@ class NonMaximumSuppression
   std::vector<DetectedObject> apply(const std::vector<DetectedObject> &);
 
 private:
-  bool isTargetLabel(const std::uint8_t);
-
   bool isTargetPairObject(const DetectedObject &, const DetectedObject &);
 
   Eigen::MatrixXd generateIoUMatrix(const std::vector<DetectedObject> &);
 
   NMSParams params_{};
-  std::vector<bool> target_class_mask_{};
+
+  double search_distance_2d_sq_{};
 };
 
 }  // namespace autoware::lidar_centerpoint

diff --git a/perception/autoware_lidar_centerpoint/lib/postprocess/non_maximum_suppression.cpp b/perception/autoware_lidar_centerpoint/lib/postprocess/non_maximum_suppression.cpp
@@ -22,22 +22,15 @@
 
 namespace autoware::lidar_centerpoint
 {
+using Label = autoware_perception_msgs::msg::ObjectClassification;
 
 void NonMaximumSuppression::setParameters(const NMSParams & params)
 {
   assert(params.search_distance_2d_ >= 0.0);
   assert(params.iou_threshold_ >= 0.0 && params.iou_threshold_ <= 1.0);
 
   params_ = params;
-  target_class_mask_ = classNamesToBooleanMask(params.target_class_names_);
-}
-
-bool NonMaximumSuppression::isTargetLabel(const uint8_t label)
-{
-  if (label >= target_class_mask_.size()) {
-    return false;
-  }
-  return target_class_mask_.at(label);
+  search_distance_2d_sq_ = params.search_distance_2d_ * params.search_distance_2d_;
 }
 
 bool NonMaximumSuppression::isTargetPairObject(
@@ -48,15 +41,15 @@ bool NonMaximumSuppression::isTargetPairObject(
   const auto label2 =
     autoware::object_recognition_utils::getHighestProbLabel(object2.classification);
 
-  if (isTargetLabel(label1) && isTargetLabel(label2)) {
-    return true;
+  // if labels are not the same, and one of them is pedestrian, do not suppress
+  if (label1 != label2 && (label1 == Label::PEDESTRIAN || label2 == Label::PEDESTRIAN)) {
+    return false;
   }
 
-  const auto search_sqr_dist_2d = params_.search_distance_2d_ * params_.search_distance_2d_;
   const auto sqr_dist_2d = autoware::universe_utils::calcSquaredDistance2d(
     autoware::object_recognition_utils::getPose(object1),
     autoware::object_recognition_utils::getPose(object2));
-  return sqr_dist_2d <= search_sqr_dist_2d;
+  return sqr_dist_2d <= search_distance_2d_sq_;
 }
 
 Eigen::MatrixXd NonMaximumSuppression::generateIoUMatrix(
@@ -73,14 +66,12 @@ Eigen::MatrixXd NonMaximumSuppression::generateIoUMatrix(
         continue;
       }
 
-      if (params_.nms_type_ == NMS_TYPE::IoU_BEV) {
-        const double iou = autoware::object_recognition_utils::get2dIoU(target_obj, source_obj);
-        triangular_matrix(target_i, source_i) = iou;
-        // NOTE: If the target object has any objects with iou > iou_threshold, it
-        // will be suppressed regardless of later results.
-        if (iou > params_.iou_threshold_) {
-          break;
-        }
+      const double iou = autoware::object_recognition_utils::get2dIoU(target_obj, source_obj);
+      triangular_matrix(target_i, source_i) = iou;
+      // NOTE: If the target object has any objects with iou > iou_threshold, it
+      // will be suppressed regardless of later results.
+      if (iou > params_.iou_threshold_) {
+        break;
       }
     }
   }
@@ -97,10 +88,9 @@ std::vector<DetectedObject> NonMaximumSuppression::apply(
   output_objects.reserve(input_objects.size());
   for (std::size_t i = 0; i < input_objects.size(); ++i) {
     const auto value = iou_matrix.row(i).maxCoeff();
-    if (params_.nms_type_ == NMS_TYPE::IoU_BEV) {
-      if (value <= params_.iou_threshold_) {
-        output_objects.emplace_back(input_objects.at(i));
-      }
+
+    if (value <= params_.iou_threshold_) {
+      output_objects.emplace_back(input_objects.at(i));
     }
   }
 

diff --git a/perception/autoware_lidar_centerpoint/schema/centerpoint.schemal.json b/perception/autoware_lidar_centerpoint/schema/centerpoint.schemal.json
@@ -42,12 +42,6 @@
               "minimum": 0.0,
               "maximum": 1.0
             },
-            "iou_nms_target_class_names": {
-              "type": "array",
-              "description": "An array of class names to be target in NMS.",
-              "default": ["CAR"],
-              "uniqueItems": true
-            },
             "iou_nms_search_distance_2d": {
               "type": "number",
               "description": "A maximum distance value to search the nearest objects.",

diff --git a/perception/autoware_lidar_centerpoint/src/node.cpp b/perception/autoware_lidar_centerpoint/src/node.cpp
@@ -80,9 +80,6 @@ LidarCenterPointNode::LidarCenterPointNode(const rclcpp::NodeOptions & node_opti
 
   {
     NMSParams p;
-    p.nms_type_ = NMS_TYPE::IoU_BEV;
-    p.target_class_names_ = this->declare_parameter<std::vector<std::string>>(
-      "post_process_params.iou_nms_target_class_names");
     p.search_distance_2d_ =
       this->declare_parameter<double>("post_process_params.iou_nms_search_distance_2d");
     p.iou_threshold_ = this->declare_parameter<double>("post_process_params.iou_nms_threshold");

diff --git a/perception/autoware_lidar_centerpoint/test/test_nms.cpp b/perception/autoware_lidar_centerpoint/test/test_nms.cpp
@@ -24,8 +24,6 @@ TEST(NonMaximumSuppressionTest, Apply)
   autoware::lidar_centerpoint::NMSParams params;
   params.search_distance_2d_ = 1.0;
   params.iou_threshold_ = 0.2;
-  params.nms_type_ = autoware::lidar_centerpoint::NMS_TYPE::IoU_BEV;
-  params.target_class_names_ = {"CAR"};
   nms.setParameters(params);
 
   std::vector<autoware::lidar_centerpoint::DetectedObject> input_objects(4);