src/tasks/_template/__init__.py

"""Template task — scene-level. **Copy this directory** and edit.

Steps:
  1. Replace TASK_NAME, display strings.
  2. Update validate_scene / evaluate_scene / load_gt_scene.
  3. Choose primary_metric.
  4. Register in src/tasks/__init__.py.
  5. Declare the task scope in the **Worker repo only** at
     ``PhysInOne-Leaderboard-Worker/task_manifest/<task>.json`` and upload
     ground truth to ``/data/Test/<benchmark>/<scene_id>_trajectory/``.
"""
from __future__ import annotations

import json
import os
from typing import Any, Dict

from src.tasks.base import TaskPlugin

TASK_NAME = "template"


def validate_scene(scene_dir: str) -> None:
    if not os.path.isfile(os.path.join(scene_dir, "prediction.json")):
        raise ValueError("prediction.json is missing from the scene ZIP root")


def evaluate_scene(scene_dir: str, gt_scene: Any) -> Dict[str, float]:
    with open(os.path.join(scene_dir, "prediction.json"), "r", encoding="utf-8") as f:
        pred = json.load(f)
    true_label = gt_scene.get("label") if isinstance(gt_scene, dict) else gt_scene
    return {"accuracy": 1.0 if pred.get("label") == true_label else 0.0}


def load_gt_scene(scene_id: str, gt_dir: str) -> Any:
    with open(os.path.join(gt_dir, "label.json"), "r", encoding="utf-8") as f:
        return json.load(f)


TASK = TaskPlugin(
    name=TASK_NAME,
    display_name="Template Task",
    description="Copy this directory to `src/tasks/<your_task>/` and then customize it.",
    expected_scene_layout=(
        "```\n"
        "<scene_id>.zip\n"
        "└── prediction.json   # {\"label\": ...}\n"
        "```\n"
        "GT is stored at `ground_truth/<scene_id>/label.json` in the private dataset."
    ),
    validate_scene_fn=validate_scene,
    evaluate_scene_fn=evaluate_scene,
    load_gt_scene_fn=load_gt_scene,
    primary_metric="accuracy",
    higher_is_better=True,
    leaderboard_columns=["accuracy"],
)