| from __future__ import annotations |
|
|
| import argparse |
| import json |
|
|
| from common import load_config |
| from dynafall.train_eval import evaluate_checkpoint |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--dataset", required=True) |
| ap.add_argument("--method", required=True) |
| ap.add_argument("--checkpoint", default=None) |
| ap.add_argument("--split", default="test") |
| ap.add_argument("--robustness", default="clean") |
| ap.add_argument("--missing-amount", type=float, default=0.0) |
| ap.add_argument("--tag", default=None) |
| ap.add_argument("--config", default="configs/default.yaml") |
| ap.add_argument("--processed-dataset", default=None) |
| ap.add_argument("--out-dir", default=None) |
| args = ap.parse_args() |
| result = evaluate_checkpoint( |
| args.dataset, |
| args.method, |
| load_config(args.config), |
| checkpoint=args.checkpoint, |
| split=args.split, |
| robustness=args.robustness, |
| missing_amount=args.missing_amount, |
| tag=args.tag, |
| processed_dataset=args.processed_dataset, |
| out_dir=args.out_dir, |
| ) |
| print(json.dumps(result, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|