delete useless info

deepmd/main.py

@@ -771,7 +771,6 @@ def main_parser() -> argparse.ArgumentParser:
         ),
     )
     parser_list_model_branch.add_argument("INPUT", help="The input multi-task pre-trained model file")
-
     return parser
 
 

deepmd/pt/entrypoints/main.py

@@ -44,9 +44,6 @@
 from deepmd.pt.train import (
     training,
 )
-from deepmd.pt.utils import (
-    env,
-)
 from deepmd.pt.utils.dataloader import (
     DpLoaderSet,
 )
@@ -313,21 +310,7 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None):
     log.info("DeepMD version: %s", __version__)
 
     if FLAGS.command == "train":
-        if FLAGS.list_model_branch:
-            assert (
-                FLAGS.finetune is not None
-            ), "Error: The '--list-model-branch' option requires the '--finetune' argument to specify the model."
-            state_dict = torch.load(FLAGS.finetune, map_location=env.DEVICE)
-            if "model" in state_dict:
-                state_dict = state_dict["model"]
-            model_params = state_dict["_extra_state"]["model_params"]
-            finetune_from_multi_task = "model_dict" in model_params
-            #  Pretrained model must be multitask mode
-            assert finetune_from_multi_task, "Error: The '--list-model-branch' option requires a multitask pretrained model. The provided model does not meet this criterion."
-            model_branch = list(model_params["model_dict"].keys())
-            log.info(f"Available model branches are {model_branch}")
-        else:
-            train(FLAGS)
+        train(FLAGS)
     elif FLAGS.command == "freeze":
         if Path(FLAGS.checkpoint_folder).is_dir():
             checkpoint_path = Path(FLAGS.checkpoint_folder)

deepmd/pt/train/training.py

@@ -83,7 +83,6 @@
 )
 
 log = logging.getLogger(__name__)
-from IPython import embed
 
 
 class Trainer:
@@ -519,7 +518,6 @@ def update_single_finetune_params(
                             for i in _random_state_dict.keys()
                             if i != "_extra_state" and f".{_model_key}." in i
                         ]
-                        embed()
                         for item_key in target_keys:
                             if _new_fitting and ".fitting_net." in item_key:
                                 # print(f'Keep {item_key} in old model!')

doc/train/finetuning.md

@@ -102,7 +102,7 @@ $ dp --pt train input.json --finetune multitask_pretrained.pt --model-branch CHO
 One can check the available model branches in multi-task pre-trained model by refering to the documentation of the pre-trained model or by using the following command:
 
 ```bash
-$ dp --pt train input.json --finetune multitask_pretrained.pt --list-model-branch
+$ dp --pt list-model-branch multitask_pretrained.pt
 ```
 
 :::