autoware_perception_evaluation

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()