| import json |
| from pathlib import Path |
|
|
| import torch |
| import yaml |
|
|
|
|
| PROJECT_ROOT = Path(__file__).resolve().parents[1] |
|
|
|
|
| def load_config(): |
| with open(PROJECT_ROOT / "config" / "config.yaml", "r", encoding="utf-8") as f: |
| return yaml.safe_load(f) |
|
|
|
|
| def main(): |
| cfg = load_config() |
| checkpoint = PROJECT_ROOT / cfg["paths"]["weight_path"] |
| result_dir = PROJECT_ROOT / cfg["paths"]["result_dir"] / cfg["train"]["save_name"] |
| metrics_path = result_dir / "metrics.json" |
|
|
| summary = { |
| "checkpoint": str(checkpoint), |
| "checkpoint_exists": checkpoint.exists(), |
| "result_dir": str(result_dir), |
| "result_dir_exists": result_dir.exists(), |
| "metrics": str(metrics_path), |
| "metrics_exists": metrics_path.exists(), |
| } |
| if checkpoint.exists(): |
| state = torch.load(checkpoint, map_location="cpu", weights_only=False) |
| if isinstance(state, dict): |
| summary["epoch"] = state.get("epoch") |
| summary["best_epoch"] = state.get("best_epoch") |
| summary["best_test_loss"] = state.get("best_test_loss") |
| summary["has_model_state"] = "model_state" in state |
| if metrics_path.exists(): |
| with open(metrics_path, "r", encoding="utf-8") as f: |
| summary["metrics_values"] = json.load(f) |
| print(json.dumps(summary, indent=2, ensure_ascii=False)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
