Fix/silence warnings in tests

src/metatrain/experimental/alchemical_model/model.py

@@ -129,7 +129,7 @@ def forward(
     def load_checkpoint(cls, path: Union[str, Path]) -> "AlchemicalModel":
 
         # Load the checkpoint
-        checkpoint = torch.load(path)
+        checkpoint = torch.load(path, weights_only=False)
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
 

src/metatrain/experimental/alchemical_model/trainer.py

@@ -349,7 +349,7 @@ def save_checkpoint(self, model, path: Union[str, Path]):
     def load_checkpoint(cls, path: Union[str, Path], train_hypers) -> "Trainer":
 
         # Load the checkpoint
-        checkpoint = torch.load(path)
+        checkpoint = torch.load(path, weights_only=False)
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
         epoch = checkpoint["epoch"]

src/metatrain/experimental/pet/model.py

@@ -114,14 +114,14 @@ def forward(
     @classmethod
     def load_checkpoint(cls, path: Union[str, Path]) -> "PET":
 
-        checkpoint = torch.load(path)
+        checkpoint = torch.load(path, weights_only=False)
         hypers = checkpoint["hypers"]
         dataset_info = checkpoint["dataset_info"]
         model = cls(
             model_hypers=hypers["ARCHITECTURAL_HYPERS"], dataset_info=dataset_info
         )
 
-        checkpoint = torch.load(path)
+        checkpoint = torch.load(path, weights_only=False)
         state_dict = checkpoint["checkpoint"]["model_state_dict"]
 
         ARCHITECTURAL_HYPERS = Hypers(model.hypers)

src/metatrain/experimental/pet/trainer.py

@@ -163,7 +163,7 @@ def train(
         else:
             load_path = self.pet_dir / "best_val_rmse_energies_model_state_dict"
 
-        state_dict = torch.load(load_path)
+        state_dict = torch.load(load_path, weights_only=False)
 
         ARCHITECTURAL_HYPERS = Hypers(model.hypers)
         raw_pet = PET(ARCHITECTURAL_HYPERS, 0.0, len(model.atomic_types))
@@ -186,7 +186,7 @@ def save_checkpoint(self, model, path: Union[str, Path]):
         # together with the hypers inside a file that will act as a metatrain
         # checkpoint
         checkpoint_path = self.pet_dir / "checkpoint"  # type: ignore
-        checkpoint = torch.load(checkpoint_path)
+        checkpoint = torch.load(checkpoint_path, weights_only=False)
         torch.save(
             {
                 "checkpoint": checkpoint,
@@ -204,7 +204,7 @@ def load_checkpoint(cls, path: Union[str, Path], train_hypers) -> "Trainer":
         # This function loads a metatrain PET checkpoint and returns a Trainer
         # instance with the hypers, while also saving the checkpoint in the
         # class
-        checkpoint = torch.load(path)
+        checkpoint = torch.load(path, weights_only=False)
         trainer = cls(train_hypers)
         trainer.pet_checkpoint = checkpoint["checkpoint"]
         return trainer

src/metatrain/experimental/soap_bpnn/model.py

@@ -287,7 +287,7 @@ def forward(
     def load_checkpoint(cls, path: Union[str, Path]) -> "SoapBpnn":
 
         # Load the checkpoint
-        checkpoint = torch.load(path)
+        checkpoint = torch.load(path, weights_only=False)
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
 

src/metatrain/experimental/soap_bpnn/trainer.py

@@ -290,7 +290,18 @@ def train(
                 targets = average_by_num_atoms(targets, systems, per_structure_targets)
 
                 train_loss_batch = loss_fn(predictions, targets)
-                train_loss_batch.backward()
+
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        action="ignore",
+                        message="second derivatives with respect to positions",
+                    )
+                    warnings.filterwarnings(
+                        action="ignore",
+                        message="second derivatives with respect to cell matrix",
+                    )
+                    train_loss_batch.backward()
+
                 optimizer.step()
 
                 if is_distributed:
@@ -409,7 +420,7 @@ def save_checkpoint(self, model, path: Union[str, Path]):
     def load_checkpoint(cls, path: Union[str, Path], train_hypers) -> "Trainer":
 
         # Load the checkpoint
-        checkpoint = torch.load(path)
+        checkpoint = torch.load(path, weights_only=False)
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
         epoch = checkpoint["epoch"]

tests/cli/test_train_model.py

@@ -449,8 +449,8 @@ def test_model_consistency_with_seed(options, monkeypatch, tmp_path, seed):
 
     train_model(options, output="model2.pt")
 
-    m1 = torch.load("model1.ckpt")
-    m2 = torch.load("model2.ckpt")
+    m1 = torch.load("model1.ckpt", weights_only=False)
+    m2 = torch.load("model2.ckpt", weights_only=False)
 
     for i in m1["model_state_dict"]:
         tensor1 = m1["model_state_dict"][i]