| |
| """Re-evaluate a selected checkpoint without ever using test data for selection.""" |
| from __future__ import annotations |
|
|
| import argparse, json |
| from pathlib import Path |
| import sys |
| import torch, yaml |
| from torch.utils.data import DataLoader |
| sys.path.insert(0, str(Path(__file__).resolve().parent)) |
| from train_detector import evaluate |
| from detector_lib import LockedLODDataset, build_model, collate |
|
|
| def main(): |
| p = argparse.ArgumentParser(); p.add_argument("--config", type=Path, required=True); p.add_argument("--dataset-root", type=Path, required=True); p.add_argument("--labels-root", type=Path, required=True); p.add_argument("--checkpoint", type=Path, required=True); p.add_argument("--output", type=Path, required=True); a=p.parse_args() |
| cfg=yaml.safe_load(a.config.read_text()); device=torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| model, processor, kind=build_model(cfg); model.load_state_dict(torch.load(a.checkpoint,map_location="cpu")["model"]); model.to(device) |
| results={} |
| for manifest in cfg["test_manifests"]: |
| ds=LockedLODDataset(a.config.parent.parent/manifest,a.dataset_root,a.labels_root) |
| results[Path(manifest).stem]=evaluate(model,processor,kind,DataLoader(ds,batch_size=1,num_workers=0,collate_fn=collate),device) |
| a.output.write_text(json.dumps(results,indent=2)+"\n") |
| if __name__ == "__main__": main() |
|
|
