update

examples/vm_sound_classification/step_3_train_model.py

@@ -186,7 +186,8 @@ def main():
 
     if args.pretrained_model is not None and os.path.exists(args.pretrained_model):
         logger.info(f"load pretrained model state dict from: {args.pretrained_model}")
-        with zipfile.ZipFile(args.pretrained_model, "r") as f_zip:
+        pretrained_model = Path(args.pretrained_model)
+        with zipfile.ZipFile(pretrained_model.as_posix(), "r") as f_zip:
             out_root = Path(tempfile.gettempdir()) / "vm_sound_classification"
             # print(out_root.as_posix())
             if out_root.exists():
@@ -194,7 +195,7 @@ def main():
             out_root.mkdir(parents=True, exist_ok=True)
             f_zip.extractall(path=out_root)
 
-        tgt_path = out_root / os.path.basename(args.pretrained_model)
+        tgt_path = out_root / pretrained_model.stem
         model_pt_file = tgt_path / "model.pt"
         with open(model_pt_file, "r") as f:
             state_dict = torch.load(f, map_location="cpu")