support custom configure_optimizers method and avoid from saving random model by default

fuse/dl/lightning/pl_module.py

@@ -17,7 +17,7 @@
 """
 
 import pytorch_lightning as pl
-from typing import Optional, Union, Tuple
+from typing import Optional, Union, Tuple, Callable
 from collections import OrderedDict
 import os
 
@@ -50,11 +50,11 @@ def __init__(
                 List[Tuple[str, OrderedDict[str, MetricBase]]],
             ]
         ] = None,
-        optimizers_and_lr_schs: Any = None,
+        optimizers_and_lr_schs: Union[dict, Callable] = None,
         callbacks: Optional[Sequence[pl.Callback]] = None,
         best_epoch_source: Optional[Union[Dict, List[Dict]]] = None,
         save_hyperparameters_kwargs: Optional[dict] = None,
-        save_model: bool = True,
+        save_model: bool = False,
         save_arguments: bool = False,
         tensorboard_sep: str = ".",
         log_unit: str = None,
@@ -63,7 +63,6 @@ def __init__(
         """
         :param model_dir: location for checkpoints and logs
         :param model: Pytorch model to use
-        :param optimizers_and_lr_schs: see pl.LightningModule.configure_optimizers for details and relevant options
         :param losses: dict of FuseMedML style losses
                Will be used for both train and validation unless validation_losses is specified.
         :param validation_losses: Optional, typically used when there are multiple validation dataloaders - each with a different loss
@@ -75,7 +74,7 @@ def __init__(
         :param test_metrics: ordereddict of FuseMedML style metrics - used for test set (must be different instances of metrics (from train_metrics)
                                    In case of multiple test dataloaders, test_metrics should be list of tuples (that keeps the same dataloaders list order),
                                    Each tuple built from test dataloader name and corresponding metrics dict.
-        :param optimizers_and_lr_schs: see pl.LightningModule.configure_optimizers return value for all options
+        :param optimizers_and_lr_schs: either a callable that follows pl.LightningModule.configure_optimizers prototype or just the output of such a function.
         :param callbacks: see pl.LightningModule.configure_callbacks return value for details
         :param best_epoch_source: Create list of pl.callbacks that saves checkpoints using (pl.callbacks.ModelCheckpoint) and print per epoch summary (fuse.dl.lightning.pl_epoch_summary.ModelEpochSummary).
                                   Either a dict with arguments to pass to ModelCheckpoint or list dicts for multiple ModelCheckpoint callbacks (to monitor and save checkpoints for more then one metric).
@@ -329,6 +328,8 @@ def configure_callbacks(self) -> Sequence[pl.Callback]:
 
     def configure_optimizers(self) -> torch.optim.Optimizer:
         """See pl.LightningModule.configure_optimizers return value for all options"""
+        if isinstance(self._optimizers_and_lr_schs, Callable):
+            return self._optimizers_and_lr_schs(self)
         return self._optimizers_and_lr_schs
 
     def set_predictions_keys(self, keys: List[str]) -> None: