feat(perception_benchmark_tool): add perception benchmark tool

benchmarking/perception_benchmark_tool/README.md

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+# perception_benchmark_tool
+
+This package contains benchmark tool for testing perception stack of Autoware.Universe with the Waymo dataset.
+
+## Installation
+
+For testing 3D Object Tracking with Waymo Dataset, follow the given steps.
+
+1- Download the Waymo dataset validation segment(.tfrecord) files from the given link.
+
+<https://waymo.com/open/download/>
+
+Just one of the validation_000x.tar files is enough for evaluation. Unpack it to the desired directory.
+
+```bash
+cd ~/Downloads/
+tar -xvf validation_validation_0000.tar
+```
+
+2- Install the Waymo Open Dataset Toolkit.
+
+```bash
+pip3 install waymo-open-dataset-tf-2-4-0
+```
+
+3- For running Autoware.Universe with the Waymo evaluation node,
+
+Lidar point clouds and camera images are encoded in the .tfrecord file. It may take about ~60-90 seconds to decode
+the data back.
+
+```bash
+ros2 launch perception_benchmark_tool benchmark_runner.launch.py
+```
+
+This command will run the perception stack with the Waymo Dataset. We will get the ground truth and prediction files in the file
+paths we give as arguments to the launch file.
+
+Lidar point clouds and camera images are encoded in the .tfrecord file. It may take about ~60-90 seconds to decode
+the data back for each segment file.
+
+4- Install Waymo Open Dataset toolkit for metric computation:
+
+Follow the given command or instruction provided by the Waymo: <https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md>
+
+```bash
+git clone https://github.com/waymo-research/waymo-open-dataset.git waymo-od
+cd waymo-od
+git checkout remotes/origin/master
+sudo apt-get install --assume-yes pkg-config zip g++ zlib1g-dev unzip python3 python3-pip
+BAZEL_VERSION=3.1.0
+wget https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/bazel-${BAZEL_VERSION}-installer-linux-x86_64.sh
+sudo bash bazel-${BAZEL_VERSION}-installer-linux-x86_64.sh
+sudo apt install build-essential
+./configure.sh
+bazel clean
+bazel build waymo_open_dataset/metrics/tools/compute_tracking_metrics_main
+```
+
+5- Evaluate tracking result
+
+```bash
+bazel-bin/waymo_open_dataset/metrics/tools/compute_tracking_metrics_main \
+/$YOUR_AUTOWARE_PATH/src/universe/autoware/benchmarking/perception_benchmark_tool/benchmarking_result/predictions.bin \
+/$YOUR_AUTOWARE_PATH/src/universe/autoware/benchmarking/perception_benchmark_tool/benchmarking_result/gt.bin
+```
+
+## Result
+
+The evaluation result of the Perception pipeline on the Waymo Dataset is presented below.
+
+### lidar_centerpoint
+
+![Screenshot from 2022-05-09 01-10-48](https://user-images.githubusercontent.com/12658936/167317879-fd1777c3-e7d4-4492-9108-673f9a2f774b.png)
+
+### lidar_apollo_instance_segmentation
+
+![Screenshot from 2022-05-09 02-05-03](https://user-images.githubusercontent.com/12658936/167319631-aa8a7f01-88d9-4db0-8e30-ae5013359a12.png)

benchmarking/perception_benchmark_tool/launch/benchmark_runner.launch.py

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+import os
+from os.path import join as joinPath
+
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+
+
+def generate_launch_description():
+
+    dataset_path_launch_arg = DeclareLaunchArgument(
+        "dataset_path",
+        default_value=joinPath(os.environ["HOME"], "validation_validation_0000"),
+        description="",
+    )
+
+    use_camera_launch_arg = DeclareLaunchArgument(
+        "use_camera",
+        default_value="False",
+        description="",
+    )
+
+    result_path_launch_arg = DeclareLaunchArgument(
+        "result_path",
+        default_value=joinPath(os.environ["HOME"], "benchmark_result"),
+        description="",
+    )
+
+    benchmark_frame_launch_arg = DeclareLaunchArgument("benchmark_frame", default_value="base_link")
+
+    launch_file_launch_arg = DeclareLaunchArgument(
+        "launch_file",
+        default_value="waymo_perception.launch.xml",
+        description="Launch file for testing perception stack",
+    )
+
+    vehicle_model_launch_arg = DeclareLaunchArgument(
+        "vehicle_model",
+        default_value="sample_vehicle",
+        description="",
+    )
+
+    sensor_model_launch_arg = DeclareLaunchArgument(
+        "sensor_model",
+        default_value="sample_sensor_kit",
+        description="",
+    )
+
+    benchmark_node = Node(
+        package="perception_benchmark_tool",
+        name="benchmark_node",
+        executable="benchmark_node",
+        output="screen",
+        parameters=[
+            {
+                "result_path": LaunchConfiguration("result_path"),
+                "benchmark_frame": LaunchConfiguration("benchmark_frame"),
+            }
+        ],
+    )
+
+    waymo_player_node = Node(
+        package="perception_benchmark_tool",
+        name="waymo_player_node",
+        executable="waymo_player_node",
+        output="screen",
+        parameters=[
+            {
+                "dataset_path": LaunchConfiguration("dataset_path"),
+                "use_camera": LaunchConfiguration("use_camera"),
+            }
+        ],
+    )
+
+    autoware_workflow_runner_node = Node(
+        package="perception_benchmark_tool",
+        name="autoware_workflow_runner_node",
+        executable="autoware_workflow_runner_node",
+        output="screen",
+        parameters=[
+            {
+                "launch_file": LaunchConfiguration("launch_file"),
+                "vehicle_model": LaunchConfiguration("vehicle_model"),
+                "sensor_model": LaunchConfiguration("sensor_model"),
+            }
+        ],
+    )
+
+    return LaunchDescription(
+        [
+            dataset_path_launch_arg,
+            use_camera_launch_arg,
+            benchmark_frame_launch_arg,
+            result_path_launch_arg,
+            launch_file_launch_arg,
+            vehicle_model_launch_arg,
+            sensor_model_launch_arg,
+            benchmark_node,
+            waymo_player_node,
+            autoware_workflow_runner_node,
+        ]
+    )

benchmarking/perception_benchmark_tool/launch/pointcloud_preprocessor.launch.py

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+# Copyright 2020 Tier IV, Inc. 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.
+
+
+import launch
+from launch.actions import DeclareLaunchArgument
+from launch.actions import OpaqueFunction
+from launch.actions import SetLaunchConfiguration
+from launch.conditions import IfCondition
+from launch.conditions import UnlessCondition
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import ComposableNodeContainer
+from launch_ros.actions import LoadComposableNodes
+from launch_ros.descriptions import ComposableNode
+
+
+def launch_setup(context, *args, **kwargs):
+
+    # set concat filter as a component
+    concat_component = ComposableNode(
+        package="pointcloud_preprocessor",
+        plugin="pointcloud_preprocessor::PointCloudConcatenateDataSynchronizerComponent",
+        name="concatenate_data",
+        remappings=[("output", "/sensing/lidar/concatenated/pointcloud")],
+        parameters=[
+            {
+                "input_topics": [
+                    "/point_cloud/front_lidar",
+                    "/point_cloud/rear_lidar",
+                    "/point_cloud/side_left_lidar",
+                    "/point_cloud/side_right_lidar",
+                    "/point_cloud/top_lidar",
+                ],
+                "output_frame": LaunchConfiguration("base_frame"),
+            }
+        ],
+        extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}],
+    )
+
+    # set container to run all required components in the same process
+    container = ComposableNodeContainer(
+        name=LaunchConfiguration("container_name"),
+        namespace="",
+        package="rclcpp_components",
+        executable=LaunchConfiguration("container_executable"),
+        composable_node_descriptions=[],
+        condition=UnlessCondition(LaunchConfiguration("use_pointcloud_container")),
+        output="screen",
+    )
+
+    target_container = (
+        container
+        if UnlessCondition(LaunchConfiguration("use_pointcloud_container")).evaluate(context)
+        else LaunchConfiguration("container_name")
+    )
+
+    # load concat or passthrough filter
+    concat_loader = LoadComposableNodes(
+        composable_node_descriptions=[concat_component],
+        target_container=target_container,
+        condition=IfCondition(LaunchConfiguration("use_concat_filter")),
+    )
+
+    return [container, concat_loader]
+
+
+def generate_launch_description():
+
+    launch_arguments = []
+
+    def add_launch_arg(name: str, default_value=None):
+        launch_arguments.append(DeclareLaunchArgument(name, default_value=default_value))
+
+    add_launch_arg("base_frame", "base_link")
+    add_launch_arg("use_multithread", "False")
+    add_launch_arg("use_intra_process", "False")
+    add_launch_arg("use_pointcloud_container", "False")
+    add_launch_arg("container_name", "pointcloud_preprocessor_container")
+
+    set_container_executable = SetLaunchConfiguration(
+        "container_executable",
+        "component_container",
+        condition=UnlessCondition(LaunchConfiguration("use_multithread")),
+    )
+
+    set_container_mt_executable = SetLaunchConfiguration(
+        "container_executable",
+        "component_container_mt",
+        condition=IfCondition(LaunchConfiguration("use_multithread")),
+    )
+
+    return launch.LaunchDescription(
+        launch_arguments
+        + [set_container_executable, set_container_mt_executable]
+        + [OpaqueFunction(function=launch_setup)]
+    )

benchmarking/perception_benchmark_tool/launch/waymo_perception.launch.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<launch>
+  <!-- Essential parameters -->
+  <arg name="vehicle_model" description="vehicle model name"/>
+  <arg name="use_pointcloud_container" default="true" description="launch pointcloud container"/>
+  <arg name="pointcloud_container_name" default="pointcloud_container"/>
+
+  <!-- Optional parameters -->
+  <arg name="launch_perception" default="true" description="launch perception"/>
+  <arg name="use_sim_time" default="false" description="use_sim_time"/>
+  <arg name="system_run_mode" default="online" description="run mode in system"/>
+  <arg name="rviz" default="true" description="launch rviz"/>
+  <arg name="rviz_config" default="$(find-pkg-share perception_benchmark_tool)/rviz/waymo.rviz" description="rviz config"/>
+
+  <!-- Global parameters -->
+  <group scoped="false">
+    <include file="$(find-pkg-share global_parameter_loader)/launch/global_params.launch.py">
+      <arg name="use_sim_time" value="$(var use_sim_time)"/>
+      <arg name="vehicle_model" value="$(var vehicle_model)"/>
+    </include>
+  </group>
+
+  <include file="$(find-pkg-share perception_benchmark_tool)/launch/pointcloud_preprocessor.launch.py">
+    <arg name="base_frame" value="base_link"/>
+    <arg name="use_intra_process" value="true"/>
+    <arg name="use_multithread" value="true"/>
+    <arg name="use_pointcloud_container" value="false"/>
+    <arg name="container_name" value="$(var pointcloud_container_name)"/>
+    <arg name="use_concat_filter" value="true"/>
+  </include>
+
+  <!-- Perception -->
+  <group if="$(var launch_perception)">
+    <include file="$(find-pkg-share tier4_perception_launch)/launch/perception.launch.xml">
+      <arg name="mode" value="lidar"/>
+      <arg name="vehicle_param_file" value="$(find-pkg-share $(var vehicle_model)_description)/config/vehicle_info.param.yaml"/>
+      <arg name="use_pointcloud_container" value="$(var use_pointcloud_container)"/>
+      <arg name="pointcloud_container_name" value="$(var pointcloud_container_name)"/>
+      <arg name="use_vector_map" value="false"/>
+      <arg name="use_pointcloud_map" value="false"/>
+      <arg name="use_object_filter" value="false"/>
+      <arg name="image_number" value="0"/>
+    </include>
+  </group>
+
+  <!-- Tools -->
+  <node pkg="rviz2" exec="rviz2" name="rviz2" output="screen" args="-d $(find-pkg-share perception_benchmark_tool)/rviz/waymo.rviz"/>
+</launch>

benchmarking/perception_benchmark_tool/package.xml

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+<?xml version="1.0"?>
+<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
+<package format="3">
+  <name>perception_benchmark_tool</name>
+  <version>0.0.0</version>
+  <description>This package benchmark Autoware perception stack on Waymo dataset</description>
+  <maintainer email="kcolak@leodrive.ai">Kaan Colak</maintainer>
+  <license>Apache License 2.0</license>
+
+  <test_depend>ament_copyright</test_depend>
+  <test_depend>ament_flake8</test_depend>
+  <test_depend>ament_pep257</test_depend>
+  <test_depend>python3-pytest</test_depend>
+
+  <exec_depend>autoware_auto_perception_msgs</exec_depend>
+  <exec_depend>global_parameter_loader</exec_depend>
+  <exec_depend>rviz2</exec_depend>
+  <exec_depend>tier4_perception_msgs</exec_depend>
+
+  <depend>cv_bridge</depend>
+  <depend>geometry_msgs</depend>
+  <depend>python3-waymo-open-dataset-tf-2-6-0-pip</depend>
+  <depend>std_srvs</depend>
+  <depend>tf2</depend>
+  <depend>tf2_geometry_msgs</depend>
+  <depend>tf2_ros</depend>
+  <depend>tf_transformations</depend>
+
+  <export>
+    <build_type>ament_python</build_type>
+  </export>
+</package>