drafting the load_model method with conversion between spk versions

src/schnetpack/interfaces/ase_interface.py

@@ -38,6 +38,7 @@
 from schnetpack.data.loader import _atoms_collate_fn
 from schnetpack.transform import CastTo32, CastTo64
 from schnetpack.units import convert_units
+from schnetpack.utils import load_model
 from schnetpack.md.utils import activate_model_stress
 
 from typing import Optional, List, Union, Dict
@@ -262,7 +263,7 @@ def _load_model(self, model_file: str) -> schnetpack.model.AtomisticModel:
 
         log.info("Loading model from {:s}".format(model_file))
         # load model and keep it on CPU, device can be changed afterwards
-        model = torch.load(model_file, map_location="cpu").to(torch.float64)
+        model = load_model(model_file, device=torch.device("cpu")).to(torch.float64)
         model = model.eval()
 
         if self.stress_key is not None:

src/schnetpack/md/calculators/schnetpack_calculator.py

@@ -2,6 +2,7 @@
 from typing import Union, List, Dict, TYPE_CHECKING
 
 import schnetpack.atomistic.response
+from schnetpack.utils import load_model
 
 if TYPE_CHECKING:
     from schnetpack.md import System
@@ -94,7 +95,7 @@ def _load_model(self, model_file: str) -> AtomisticModel:
 
         log.info("Loading model from {:s}".format(model_file))
         # load model and keep it on CPU, device can be changed afterwards
-        model = torch.load(model_file, map_location="cpu").to(torch.float64)
+        model = load_model(model_file, device=torch.device("cpu")).to(torch.float64)
         model = model.eval()
 
         if self.stress_key is not None:

src/schnetpack/model/base.py

@@ -2,6 +2,7 @@
 
 from typing import Dict, Optional, List
 
+import schnetpack as spk
 from schnetpack.transform import Transform
 import schnetpack.properties as properties
 from schnetpack.utils import as_dtype
@@ -78,6 +79,7 @@ def __init__(
         self.postprocessors = nn.ModuleList(postprocessors)
         self.required_derivatives: Optional[List[str]] = None
         self.model_outputs: Optional[List[str]] = None
+        self.spk_version = spk.__version__
 
     def collect_derivatives(self) -> List[str]:
         self.required_derivatives = None

src/schnetpack/utils/__init__.py

@@ -1,3 +1,4 @@
+from .compatibility import *
 import importlib
 import torch
 from typing import Type, Union, List

src/schnetpack/utils/compatibility.py

@@ -0,0 +1,48 @@
+import torch
+import warnings
+
+
+__all__ = ["load_model"]
+
+
+def load_model(model_path, device="cpu", **kwargs):
+    """
+    Load a SchNetPack model from a Torch file, enabling compatibility with models trained using earlier versions of
+    SchNetPack. This function imports the old model and automatically updates it to the format used in the current
+    SchNetPack version. To ensure proper functionality, the Torch model object must include a version tag, such as
+    spk_version="2.0.4".
+
+    Args:
+        model_path (str): Path to the model file.
+        device (torch.device): Device on which the model should be loaded.
+        **kwargs: Additional arguments for the model loading.
+
+    Returns:
+        torch.nn.Module: Loaded model.
+    """
+
+    def _convert_from_older(model):
+        model.spk_version = "2.0.4"
+        return model
+
+    def _convert_from_v2_0_4(model):
+        if not hasattr(model.representation, "electronic_embeddings"):
+            model.representation.electronic_embeddings = []
+        model.spk_version = (
+            "latest"  # TODO: replace by latest pypi version once available
+        )
+        return model
+
+    model = torch.load(model_path, map_location=device, **kwargs)
+
+    if not hasattr(model, "spk_version"):
+        # make warning that model has no version information
+        warnings.warn(
+            "Model was saved without version information. Conversion to current version may fail."
+        )
+        model = _convert_from_older(model)
+
+    if model.spk_version == "2.0.4":
+        model = _convert_from_v2_0_4(model)
+
+    return model