X_SSL_Net/tools/summarize_results.py

from __future__ import annotations

import argparse
import csv
from pathlib import Path
from typing import Any

import torch


def _infer_mode(path: Path) -> str:
    parts = set(path.parts)
    if "supervised" in parts:
        return "supervised"
    return "unknown"


def _infer_dataset(ckpt: dict[str, Any], path: Path) -> str:
    cfg = ckpt.get("cfg", {})
    dataset_cfg = cfg.get("dataset", {})
    dataset_name = dataset_cfg.get("dataset_name") or dataset_cfg.get("name")
    if dataset_name:
        return str(dataset_name)

    parts = path.parts
    if "supervised" in parts:
        idx = parts.index("supervised")
        if idx + 1 < len(parts):
            return parts[idx + 1]
    return "unknown"


def _infer_ratio(ckpt: dict[str, Any], path: Path) -> str:
    return "-"


def _extract_metric(metrics: dict[str, Any], *names: str) -> float | None:
    for name in names:
        value = metrics.get(name)
        if value is not None:
            return float(value)
    return None


def collect_rows(outputs_dir: Path) -> list[dict[str, Any]]:
    rows: list[dict[str, Any]] = []
    for best_path in sorted(outputs_dir.rglob("best.pth")):
        ckpt = torch.load(best_path, map_location="cpu")
        metrics = ckpt.get("metrics", {}) or {}
        row = {
            "dataset": _infer_dataset(ckpt, best_path),
            "mode": _infer_mode(best_path),
            "ratio": _infer_ratio(ckpt, best_path),
            "epoch": ckpt.get("epoch"),
            "best_metric": ckpt.get("best_metric"),
            "dice": _extract_metric(metrics, "val_dice", "dice"),
            "iou": _extract_metric(metrics, "val_iou", "val_miou", "iou", "miou"),
            "checkpoint": str(best_path),
        }
        rows.append(row)
    return rows


def write_csv(rows: list[dict[str, Any]], path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    fieldnames = ["dataset", "mode", "ratio", "epoch", "best_metric", "dice", "iou", "checkpoint"]
    with path.open("w", encoding="utf-8", newline="") as handle:
        writer = csv.DictWriter(handle, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(rows)


def write_markdown(rows: list[dict[str, Any]], path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    lines = [
        "# 实验结果汇总",
        "",
        "| dataset | mode | ratio | epoch | best_metric | dice | iou | checkpoint |",
        "| --- | --- | --- | --- | --- | --- | --- | --- |",
    ]
    for row in rows:
        lines.append(
            f"| {row['dataset']} | {row['mode']} | {row['ratio']} | {row['epoch']} | "
            f"{row['best_metric']} | {row['dice']} | {row['iou']} | {row['checkpoint']} |"
        )
    if not rows:
        lines.append("| - | - | - | - | - | - | - | - |")
    path.write_text("\n".join(lines) + "\n", encoding="utf-8")


def main() -> None:
    parser = argparse.ArgumentParser(description="Summarize best experiment results from best.pth files.")
    parser.add_argument("--outputs-dir", default="outputs", help="Root output directory")
    parser.add_argument("--results-dir", default="results", help="Directory to write summary tables")
    args = parser.parse_args()

    outputs_dir = Path(args.outputs_dir)
    results_dir = Path(args.results_dir)
    rows = collect_rows(outputs_dir)

    csv_path = results_dir / "experiment_summary.csv"
    md_path = results_dir / "experiment_summary.md"
    write_csv(rows, csv_path)
    write_markdown(rows, md_path)

    print({"num_results": len(rows), "csv": str(csv_path), "markdown": str(md_path)})


if __name__ == "__main__":
    main()