[to #42322933] support restore best checkpoint after training

1. 支持训练完成后自动恢复best ckpt，方便在不同测试集上进行测试
2. build_optimizer/build_lr_scheduler改为成员函数，方便重载（如模型分层lr）
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10348255

modelscope/trainers/hooks/checkpoint_hook.py

@@ -216,6 +216,7 @@ class BestCkptSaverHook(CheckpointHook):
         by_epoch (bool): Save best checkpoints by epoch or by iteration.
         save_optimizer (bool): Whether to save optimizer state dict.  Default: True.
         save_dir (str): Output directory to save best checkpoint.
+        restore_best (bool): Whether to restore the best checkpoint after training.
     """
 
     PRIORITY = Priority.LOW
@@ -228,6 +229,7 @@ class BestCkptSaverHook(CheckpointHook):
                  save_optimizer=True,
                  save_dir=None,
                  save_file_name=None,
+                 restore_best=False,
                  interval=0):
         assert rule in ['max', 'min'], 'Only support "max" or "min" rule now.'
         super().__init__(
@@ -241,6 +243,7 @@ class BestCkptSaverHook(CheckpointHook):
         self._best_metric = None
         self._best_ckpt_file = None
         self.save_file_name = save_file_name
+        self.restore_best = restore_best
 
     def _should_save(self, trainer):
         return self._is_best_metric(trainer.metric_values)
@@ -305,3 +308,7 @@ class BestCkptSaverHook(CheckpointHook):
             self.logger.warn(
                 'The state_dict is not available, the best metric value will be affected.'
             )
+
+    def after_run(self, trainer):
+        if self.restore_best:
+            self.load_checkpoint(self._best_ckpt_file, trainer)

modelscope/trainers/trainer.py

@@ -664,6 +664,12 @@ class EpochBasedTrainer(BaseTrainer):
         dataset = self.to_task_dataset(torch_dataset, mode)
         return dataset
 
+    def build_optimizer(self, cfg: ConfigDict, default_args: dict = None):
+        return build_optimizer(self.model, cfg=cfg, default_args=default_args)
+
+    def build_lr_scheduler(self, cfg: ConfigDict, default_args: dict = None):
+        return build_lr_scheduler(cfg=cfg, default_args=default_args)
+
     def create_optimizer_and_scheduler(self):
         """ Create optimizer and lr scheduler
 
@@ -680,7 +686,7 @@ class EpochBasedTrainer(BaseTrainer):
         optim_options = {}
         if optimizer_cfg is not None:
             optim_options = optimizer_cfg.pop('options', {})
-            optimizer = build_optimizer(self.model, cfg=optimizer_cfg)
+            optimizer = self.build_optimizer(cfg=optimizer_cfg)
 
         if lr_scheduler is None:
             lr_scheduler_cfg = self.cfg.train.get('lr_scheduler', None)
@@ -691,7 +697,7 @@ class EpochBasedTrainer(BaseTrainer):
         if lr_scheduler_cfg is not None:
             assert optimizer is not None
             lr_options = lr_scheduler_cfg.pop('options', {})
-            lr_scheduler = build_lr_scheduler(
+            lr_scheduler = self.build_lr_scheduler(
                 cfg=lr_scheduler_cfg, default_args={'optimizer': optimizer})
 
         self.optimizer = optimizer