| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
|
|
| def summarize_variant(root: Path, tag: str) -> dict: |
| ckpt_dir = root / tag / "checkpoint-100000" |
| aggregate_files = sorted(ckpt_dir.glob("val_psnr_*.json")) |
| if aggregate_files: |
| data = json.loads(aggregate_files[-1].read_text(encoding="utf-8")) |
| return { |
| "tag": tag, |
| "samples": data.get("num_samples", 0), |
| "psnr_mean": data.get("psnr_mean"), |
| "psnr_std": data.get("psnr_std"), |
| "psnr_min": data.get("psnr_min"), |
| "psnr_max": data.get("psnr_max"), |
| "complete": True, |
| } |
|
|
| metrics_paths = sorted(ckpt_dir.glob("val*/metrics.json")) |
| psnr_values = [] |
| for path in metrics_paths: |
| data = json.loads(path.read_text(encoding="utf-8")) |
| value = data.get("psnr_mean") |
| if value is not None and np.isfinite(value): |
| psnr_values.append(float(value)) |
|
|
| if not psnr_values: |
| return { |
| "tag": tag, |
| "samples": 0, |
| "psnr_mean": None, |
| "psnr_std": None, |
| "psnr_min": None, |
| "psnr_max": None, |
| "complete": False, |
| } |
|
|
| values = np.array(psnr_values, dtype=np.float64) |
| return { |
| "tag": tag, |
| "samples": int(values.size), |
| "psnr_mean": float(values.mean()), |
| "psnr_std": float(values.std()), |
| "psnr_min": float(values.min()), |
| "psnr_max": float(values.max()), |
| "complete": False, |
| } |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description="Summarize query val PSNR eval outputs.") |
| parser.add_argument("--root", type=Path, default=Path("/workspace/Ctrl-World-Graph/eval_query_val")) |
| parser.add_argument( |
| "--tags", |
| nargs="*", |
| default=[ |
| "gps_query", |
| "gine_query", |
| "gatv2_query", |
| "transformer_query", |
| "edge_transformer_query", |
| "hybrid_gine_transformer_query", |
| ], |
| ) |
| parser.add_argument("--out", type=Path, default=None) |
| args = parser.parse_args() |
|
|
| rows = [summarize_variant(args.root, tag) for tag in args.tags] |
| rows.sort(key=lambda row: float("-inf") if row["psnr_mean"] is None else row["psnr_mean"], reverse=True) |
|
|
| header = "tag\tsamples\tcomplete\tpsnr_mean\tpsnr_std\tpsnr_min\tpsnr_max" |
| lines = [header] |
| for row in rows: |
| fmt = lambda value: "NA" if value is None else f"{value:.4f}" |
| lines.append( |
| f"{row['tag']}\t{row['samples']}\t{row['complete']}\t" |
| f"{fmt(row['psnr_mean'])}\t{fmt(row['psnr_std'])}\t" |
| f"{fmt(row['psnr_min'])}\t{fmt(row['psnr_max'])}" |
| ) |
| text = "\n".join(lines) |
| print(text) |
|
|
| if args.out is not None: |
| args.out.parent.mkdir(parents=True, exist_ok=True) |
| args.out.write_text(text + "\n", encoding="utf-8") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|