tier4 / autoware_perception_evaluation

autoware_perception_evaluation

perception_eval is a tool to evaluate perception tasks.

Documents

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Overview

Evaluate Perception & Sensing task

3D tasks

Task	Metrics	Sub-metrics
Detection	mAP	AP, APH
Tracking	CLEAR	MOTA, MOTP, IDswitch
Prediction	WIP	WIP
Sensing	Check Pointcloud	Detection Area & Non-detection Area

2D tasks

Task	Metrics	Sub-metrics
Detection2D	mAP	AP
Tracking2D	CLEAR	MOTA, MOTP, IDswitch
Classification2D	Accuracy	Accuracy, Precision, Recall, F1score

Dataset format

We support T4Dataset format. This has same structure with NuScenes. The expected dataset directory tree is shown as below.

data_root/
    │── annotation/     ... annotation information in json format.
    │   │── sample.json
    │   │── sample_data.json
    │   │── sample_annotation.json
    │   └── ...
    └── data/           ... raw data.
        │── LIDAR_CONCAT/  # LIDAR_TOP is also OK.
        └── CAM_**/

Using perception_eval

Evaluate with ROS

perception_eval is mainly used in tier4/driving_log_replayer that is a tool to evaluate output of autoware. If you want to evaluate your perception results through ROS, use driving_log_replayer or refer test/perception_lsim.py.

Evaluate with your ML model

This is a simple example to evaluate your 3D detection ML model. Basically, most parts of the codes are same with test/perception_lsim.py, so please refer it.

from perception_eval.config import PerceptionEvaluationConfig
from perception_eval.manager import PerceptionEvaluationManager
from perception_eval.common.object import DynamicObject
from perception_eval.evaluation.result.perception_frame_config import CriticalObjectFilterConfig
from perception_eval.evaluation.result.perception_frame_config import PerceptionPassFailConfig

# REQUIRED:
#   dataset_path: str
#   model: Your 3D ML model

evaluation_config = PerceptionEvaluationConfig(
    dataset_paths=[dataset_path],
    frame_id="base_link",
    result_root_directory="./data/result",
    evaluation_config_dict={"evaluation_task": "detection",...},
    load_raw_data=True,
)

# initialize Evaluation Manager
evaluator = PerceptionEvaluationManager(evaluation_config=evaluation_config)

critical_object_filter_config = CriticalObjectFilterConfig(...)
pass_fail_config = PerceptionPassFailConfig(...)

for frame in datasets:
    unix_time = frame.unix_time
    pointcloud: numpy.ndarray = frame.raw_data["lidar"]
    outputs = model(pointcloud)
    # create a list of estimated objects with your model's outputs
    estimated_objects = [DynamicObject(unix_time=unix_time, ...) for out in outputs]
    # add frame result
    evaluator.add_frame_result(
        unix_time=unix_time,
        ground_truth_now_frame=frame,
        estimated_objects=estimated_objects,
        critical_object_filter_config=critical_object_filter_config,
        frame_pass_fail_config=pass_fail_config,
    )

scene_score = evaluator.get_scene_result()