Add an additional attribute to ModelCheckpoint to keep track of the b…

…est model's path

Currently, only the best metric value is directly tracked. This new attribute will help in uses cases where the trained model needs to be used or tracked right after training.

pytorch_lightning/callbacks/model_checkpoint.py

@@ -112,6 +112,7 @@ def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', ve
         # {filename: monitor}
         self.kth_best_model = ''
         self.best = 0
+        self.best_model = ''
         self.save_function = None
 
         torch_inf = torch.tensor(np.Inf)
@@ -265,7 +266,8 @@ def _do_check_save(self, filepath, current, epoch):
             self.kth_value = self.best_k_models[self.kth_best_model]
 
         _op = min if self.mode == 'min' else max
-        self.best = _op(self.best_k_models.values())
+        self.best_model = _op(self.best_k_models, key=self.best_k_models.get)
+        self.best = self.best_k_models[self.best_model]
 
         if self.verbose > 0:
             log.info(

pytorch_lightning/trainer/training_io.py

@@ -315,6 +315,7 @@ def dump_checkpoint(self, weights_only: bool = False):
         if not weights_only:
             if self.checkpoint_callback:
                 checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best
+                checkpoint['checkpoint_callback_best_model'] = self.checkpoint_callback.best_model
 
             if self.early_stop_callback:
                 checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait
@@ -398,10 +399,11 @@ def restore_training_state(self, checkpoint):
                 ' This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.'
             )
 
-        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
+        if self.checkpoint_callback:
             self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']
+            self.checkpoint_callback.best_model = checkpoint['checkpoint_callback_best_model']
 
-        if self.early_stop_callback is not None and self.early_stop_callback is not False:
+        if self.early_stop_callback:
             self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']
             self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']