scripts/evaluation_prediction.py

import argparse
import json
import re
from tqdm import tqdm


SUBTASK_CATEGORIES = {
    "speed": ["KEEP", "ACCELERATE", "DECELERATE", "STOP"],
    "path": ["LEFT", "RIGHT", "STRAIGHT", "UNKNOWN"],
}
REQUIRED_SUBTASKS = set(SUBTASK_CATEGORIES)


def clean_text(s: str):
    """Normalize whitespace and punctuation."""
    if not isinstance(s, str):
        return ""
    s = s.strip()
    s = re.sub(r"[.\n\r]+", " ", s)
    s = re.sub(r"\s+", " ", s)
    return s.strip()


def parse_prediction(text, subtask):
    """
    Parse one split-task prediction:
    "B ACCELERATE" -> "ACCELERATE"
    "C STRAIGHT" -> "STRAIGHT"
    """
    if subtask not in SUBTASK_CATEGORIES:
        raise ValueError(f"Unexpected prediction subtask: {subtask!r}")

    text = clean_text(text).upper()
    categories = SUBTASK_CATEGORIES[subtask]
    matches = [
        word for word in categories
        if re.search(rf"(?<![A-Z_]){re.escape(word)}(?![A-Z_])", text)
    ]
    return matches[0] if len(matches) == 1 else None


def group_split_samples(data):
    groups = {}
    for item in data:
        original_id = item.get("original_id")
        subtask = item.get("subtask")
        if not original_id:
            raise ValueError("Prediction sample is missing original_id")
        if subtask not in SUBTASK_CATEGORIES:
            raise ValueError(f"Missing or invalid prediction subtask: {subtask!r}")

        group = groups.setdefault(original_id, {})
        if subtask in group:
            raise ValueError(f"Duplicate prediction subtask {subtask!r} for {original_id}")
        group[subtask] = item

    for original_id, group in groups.items():
        if set(group) != REQUIRED_SUBTASKS:
            raise ValueError(f"Incomplete prediction subtask pair for {original_id}: {sorted(group)}")
    return groups


def evaluate(pred_json):
    with open(pred_json, "r", encoding="utf-8") as f:
        data = json.load(f)

    if not data:
        raise ValueError(f"No samples found in {pred_json}")

    groups = group_split_samples(data)
    totals = {subtask: 0 for subtask in SUBTASK_CATEGORIES}
    correct = {subtask: 0 for subtask in SUBTASK_CATEGORIES}
    classwise = {
        subtask: {category: {"total": 0, "correct": 0} for category in categories}
        for subtask, categories in SUBTASK_CATEGORIES.items()
    }
    mismatch_examples = []
    joint_correct = 0

    for original_id, group in tqdm(groups.items(), desc="Evaluating prediction pairs"):
        is_joint_correct = True
        for subtask, item in group.items():
            gt_raw = item["conversations"][1]["value"]
            pred_raw = item.get("model_output", "")

            gt = parse_prediction(gt_raw, subtask)
            pred = parse_prediction(pred_raw, subtask)
            if gt is None:
                raise ValueError(f"Invalid {subtask} ground-truth answer: {gt_raw!r}")

            totals[subtask] += 1
            classwise[subtask][gt]["total"] += 1
            is_correct = gt == pred
            is_joint_correct = is_joint_correct and is_correct
            if is_correct:
                correct[subtask] += 1
                classwise[subtask][gt]["correct"] += 1
            else:
                mismatch_examples.append({
                    "original_id": original_id,
                    "image": item["image"],
                    "bbox_2d": item.get("bbox_2d", {}),
                    "subtask": subtask,
                    "gt": gt,
                    "pred": pred,
                    "model_output": pred_raw,
                })
        joint_correct += is_joint_correct

    total = sum(totals.values())
    original_questions = len(groups)
    overall_acc = sum(correct.values()) / total * 100
    joint_acc = joint_correct / original_questions * 100

    print(f"\nTotal samples: {total}")
    print(f"Original questions: {original_questions}")
    for subtask in SUBTASK_CATEGORIES:
        accuracy = correct[subtask] / totals[subtask] * 100 if totals[subtask] else 0.0
        print(f"{subtask.upper()} accuracy: {accuracy:.2f}% ({correct[subtask]}/{totals[subtask]})")
    print(f"Overall accuracy: {overall_acc:.2f}%")
    print(f"Joint accuracy: {joint_acc:.2f}% ({joint_correct}/{original_questions})")
    print(f"Wrong examples: {len(mismatch_examples)}")

    result_summary = {
        "total": total,
        "original_questions": original_questions,
        "overall_accuracy (%)": round(overall_acc, 2),
        "joint_correct": joint_correct,
        "joint_accuracy (%)": round(joint_acc, 2),
        "subtasks": {
            subtask: {
                "total": totals[subtask],
                "accuracy (%)": round(correct[subtask] / totals[subtask] * 100, 2) if totals[subtask] else None,
                "classwise": {
                    category: round(stats["correct"] / stats["total"] * 100, 2) if stats["total"] else None
                    for category, stats in classwise[subtask].items()
                },
            }
            for subtask in SUBTASK_CATEGORIES
        },
    }

    error_path = pred_json.replace(".json", "_errors.json")
    with open(error_path, "w", encoding="utf-8") as f:
        json.dump(mismatch_examples, f, indent=2, ensure_ascii=False)

    result_path = pred_json.replace(".json", "_accuracy.json")
    with open(result_path, "w", encoding="utf-8") as f:
        json.dump(result_summary, f, indent=2, ensure_ascii=False)

    print(f"\nError samples saved to {error_path}")
    print(f"Accuracy summary saved to {result_path}")

    return result_summary


def parse_args():
    parser = argparse.ArgumentParser(description="Evaluate EventDrive prediction task results.")
    parser.add_argument("--pred-json", required=True, help="Path to prediction JSON with model_output fields.")
    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    evaluate(args.pred_json)