Set weights_only=False when loading ckpts, since Lightning now defers to torch's default (True)

See PR on this change: Lightning-AI/pytorch-lightning#21072

Author: nathanpainchaud

src/lightning_hydra_template/eval.py

@@ -57,7 +57,7 @@ def evaluate(cfg: DictConfig) -> tuple[dict[str, Any], dict[str, Any]]:
         log_hyperparameters(object_dict)
 
     log.info("Starting testing!")
-    trainer.test(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path)
+    trainer.test(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path, weights_only=False)
 
     # for predictions use trainer.predict(...)
     # predictions = trainer.predict(model=model, dataloaders=dataloaders, ckpt_path=cfg.ckpt_path)

src/lightning_hydra_template/train.py

@@ -74,7 +74,7 @@ def train(cfg: DictConfig) -> tuple[dict[str, Any], dict[str, Any]]:
 
     if cfg.get("train"):
         log.info("Starting training!")
-        trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))
+        trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"), weights_only=False)
 
     train_metrics = trainer.callback_metrics
 
@@ -84,7 +84,7 @@ def train(cfg: DictConfig) -> tuple[dict[str, Any], dict[str, Any]]:
         if ckpt_path == "":
             log.warning("Best ckpt not found! Using current weights for testing...")
             ckpt_path = None
-        trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
+        trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path, weights_only=False)
         log.info(f"Best ckpt path: {ckpt_path}")
 
     test_metrics = trainer.callback_metrics