fix: remove warning about earlystop and save best model (#73)

Author: ydcjeff

templates/_base/_sidebar.py

@@ -152,11 +152,6 @@ def _save_best_model_by_val_score_options(config):
         "Save the best model by evaluation score",
         value=False,
     )
-    if config["save_best_model_by_val_score"]:
-        st.warning(
-            "Please make sure to pass argument to `metric_name` parameter of `get_handlers` in `main.py`."
-            " Otherwise it can result KeyError.",
-        )
     st.markdown("---")
 
 
@@ -171,8 +166,4 @@ def _add_early_stopping_by_val_score_options(config):
             min_value=1,
             value=3,
         )
-        st.warning(
-            "Please make sure to pass argument to `es_metric_name` parameter of `get_handlers` in `main.py`."
-            " Otherwise it can result KeyError.",
-        )
     st.markdown("---")

templates/gan/main.py

@@ -109,9 +109,9 @@ def run(local_rank: int, config: Any, *args: Any, **kwargs: Any):
         config=config,
         model={'netD', netD, 'netG', netG},
         train_engine=train_engine,
-        eval_engine=None,
-        metric_name=None,
-        es_metric_name=None,
+        eval_engine=train_engine,
+        metric_name='errD',
+        es_metric_name='errD',
         to_save=to_save,
         lr_scheduler=lr_scheduler,
         output_names=["errD", "errG", "D_x", "D_G_z1", "D_G_z2"],

templates/image_classification/main.py

@@ -122,13 +122,7 @@ def run(local_rank: int, config: Any, *args: Any, **kwargs: Any):
         train_engine=train_engine,
         eval_engine=eval_engine,
         metric_name="eval_accuracy",
-        # TODO : replace with the metric name to save the best model
-        # if you check `Save the best model by evaluation score` otherwise leave it None
-        # metric must be in eval_engine.state.metrics.
-        es_metric_name=None,
-        # TODO : replace with the metric name to early stop
-        # if you check `Early stop the training by evaluation score` otherwise leave it None
-        # metric must be in eval_engine.state.metrics.
+        es_metric_name="eval_accuracy",
         to_save=to_save,
         lr_scheduler=lr_scheduler,
         output_names=None,