Fix fine-tuning entries bug when doing restart. (#3616)

deepmodeling · Mar 28, 2024 · 6da8eef · 6da8eef
1 parent f16d543
commit 6da8eef
Showing 1 changed file with 41 additions and 37 deletions.
diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py
@@ -558,46 +558,50 @@ def update_single_finetune_params(
                     ]
                 self.wrapper.load_state_dict(state_dict)
 
-                def single_model_finetune(
-                    _model,
-                    _model_params,
-                    _sample_func,
-                ):
-                    old_type_map, new_type_map = (
-                        _model_params["type_map"],
-                        _model_params["new_type_map"],
-                    )
-                    if isinstance(_model, EnergyModel):
-                        _model.change_out_bias(
-                            _sample_func,
-                            bias_adjust_mode=_model_params.get(
-                                "bias_adjust_mode", "change-by-statistic"
-                            ),
-                            origin_type_map=new_type_map,
-                            full_type_map=old_type_map,
-                        )
-                    else:
-                        # need to updated
-                        pass
+                if finetune_model is not None:
 
-                # finetune
-                if not self.multi_task:
-                    single_model_finetune(
-                        self.model, model_params, self.get_sample_func
-                    )
-                else:
-                    for model_key in self.model_keys:
-                        if model_key in self.finetune_links:
-                            log.info(
-                                f"Model branch {model_key} will be fine-tuned. This may take a long time..."
-                            )
-                            single_model_finetune(
-                                self.model[model_key],
-                                model_params["model_dict"][model_key],
-                                self.get_sample_func[model_key],
+                    def single_model_finetune(
+                        _model,
+                        _model_params,
+                        _sample_func,
+                    ):
+                        old_type_map, new_type_map = (
+                            _model_params["type_map"],
+                            _model_params["new_type_map"],
+                        )
+                        if isinstance(_model, EnergyModel):
+                            _model.change_out_bias(
+                                _sample_func,
+                                bias_adjust_mode=_model_params.get(
+                                    "bias_adjust_mode", "change-by-statistic"
+                                ),
+                                origin_type_map=new_type_map,
+                                full_type_map=old_type_map,
                             )
                         else:
-                            log.info(f"Model branch {model_key} will resume training.")
+                            # need to updated
+                            pass
+
+                    # finetune
+                    if not self.multi_task:
+                        single_model_finetune(
+                            self.model, model_params, self.get_sample_func
+                        )
+                    else:
+                        for model_key in self.model_keys:
+                            if model_key in self.finetune_links:
+                                log.info(
+                                    f"Model branch {model_key} will be fine-tuned. This may take a long time..."
+                                )
+                                single_model_finetune(
+                                    self.model[model_key],
+                                    model_params["model_dict"][model_key],
+                                    self.get_sample_func[model_key],
+                                )
+                            else:
+                                log.info(
+                                    f"Model branch {model_key} will resume training."
+                                )
 
         if init_frz_model is not None:
             frz_model = torch.jit.load(init_frz_model, map_location=DEVICE)