Use return type typing_extensions.Self for class methods

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.10
+    rev: v0.5.1
     hooks:
       - id: ruff
         args: [--fix]
@@ -48,7 +48,7 @@ repos:
           - svelte
 
   - repo: https://github.com/pre-commit/mirrors-eslint
-    rev: v9.5.0
+    rev: v9.6.0
     hooks:
       - id: eslint
         types: [file]

chgnet/data/dataset.py

@@ -19,6 +19,8 @@
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
+    from typing_extensions import Self
+
     from chgnet import TrainTask
 
 warnings.filterwarnings("ignore")
@@ -97,7 +99,7 @@ def from_vasp(
         save_path: str | None = None,
         graph_converter: CrystalGraphConverter | None = None,
         shuffle: bool = True,
-    ) -> StructureData:
+    ) -> Self:
         """Parse VASP output files into structures and labels and feed into the dataset.
 
         Args:

chgnet/graph/converter.py

@@ -14,6 +14,7 @@
 
 if TYPE_CHECKING:
     from pymatgen.core import Structure
+    from typing_extensions import Self
 
 try:
     from chgnet.graph.cygraph import make_graph
@@ -285,6 +286,6 @@ def as_dict(self) -> dict[str, str | float]:
         }
 
     @classmethod
-    def from_dict(cls, dct: dict) -> CrystalGraphConverter:
+    def from_dict(cls, dct: dict) -> Self:
         """Create converter from dictionary."""
-        return CrystalGraphConverter(**dct)
+        return cls(**dct)

chgnet/graph/crystalgraph.py

@@ -1,11 +1,14 @@
 from __future__ import annotations
 
 import os
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import torch
 from torch import Tensor
 
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
 datatype = torch.float32
 
 
@@ -152,7 +155,7 @@ def save(self, fname: str | None = None, save_dir: str = ".") -> str:
         return save_name
 
     @classmethod
-    def from_file(cls, file_name: str) -> CrystalGraph:
+    def from_file(cls, file_name: str) -> Self:
         """Load a crystal graph from a file.
 
         Args:
@@ -164,9 +167,9 @@ def from_file(cls, file_name: str) -> CrystalGraph:
         return torch.load(file_name)
 
     @classmethod
-    def from_dict(cls, dic: dict[str, Any]) -> CrystalGraph:
+    def from_dict(cls, dic: dict[str, Any]) -> Self:
         """Load a CrystalGraph from a dictionary."""
-        return CrystalGraph(**dic)
+        return cls(**dic)
 
     def __repr__(self) -> str:
         """String representation of the graph."""

chgnet/model/dynamics.py

@@ -31,6 +31,7 @@
 if TYPE_CHECKING:
     from ase.io import Trajectory
     from ase.optimize.optimize import Optimizer
+    from typing_extensions import Self
 
 # We would like to thank M3GNet develop team for this module
 # source: https://github.com/materialsvirtuallab/m3gnet
@@ -94,11 +95,9 @@ def __init__(
         print(f"CHGNet will run on {self.device}")
 
     @classmethod
-    def from_file(
-        cls, path: str, use_device: str | None = None, **kwargs
-    ) -> CHGNetCalculator:
+    def from_file(cls, path: str, use_device: str | None = None, **kwargs) -> Self:
         """Load a user's CHGNet model and initialize the Calculator."""
-        return CHGNetCalculator(
+        return cls(
             model=CHGNet.from_file(path),
             use_device=use_device,
             **kwargs,

chgnet/model/model.py

@@ -25,6 +25,8 @@
 from chgnet.utils import determine_device
 
 if TYPE_CHECKING:
+    from typing_extensions import Self
+
     from chgnet import PredTask
 
 module_dir = os.path.dirname(os.path.abspath(__file__))
@@ -661,17 +663,17 @@ def todict(self) -> dict:
         return {"model_name": type(self).__name__, "model_args": self.model_args}
 
     @classmethod
-    def from_dict(cls, dct: dict, **kwargs) -> CHGNet:
+    def from_dict(cls, dct: dict, **kwargs) -> Self:
         """Build a CHGNet from a saved dictionary."""
-        chgnet = CHGNet(**dct["model_args"], **kwargs)
+        chgnet = cls(**dct["model_args"], **kwargs)
         chgnet.load_state_dict(dct["state_dict"])
         return chgnet
 
     @classmethod
-    def from_file(cls, path: str, **kwargs) -> CHGNet:
+    def from_file(cls, path: str, **kwargs) -> Self:
         """Build a CHGNet from a saved file."""
         state = torch.load(path, map_location=torch.device("cpu"))
-        return CHGNet.from_dict(state["model"], **kwargs)
+        return cls.from_dict(state["model"], **kwargs)
 
     @classmethod
     def load(
@@ -681,7 +683,7 @@ def load(
         use_device: str | None = None,
         check_cuda_mem: bool = False,
         verbose: bool = True,
-    ) -> CHGNet:
+    ) -> Self:
         """Load pretrained CHGNet model.
 
         Args:
@@ -777,7 +779,7 @@ def from_graphs(
         angle_basis_expansion: nn.Module,
         *,
         compute_stress: bool = False,
-    ) -> BatchedGraph:
+    ) -> Self:
         """Featurize and assemble a list of graphs.
 
         Args:

chgnet/trainer/trainer.py

@@ -29,6 +29,7 @@
 
 if TYPE_CHECKING:
     from torch.utils.data import DataLoader
+    from typing_extensions import Self
 
     from chgnet import TrainTask
 
@@ -645,14 +646,14 @@ def save_checkpoint(self, epoch: int, mae_error: dict, save_dir: str) -> None:
             )
 
     @classmethod
-    def load(cls, path: str) -> Trainer:
+    def load(cls, path: str) -> Self:
         """Load trainer state_dict."""
         state = torch.load(path, map_location=torch.device("cpu"))
         model = CHGNet.from_dict(state["model"])
         print(f"Loaded model params = {sum(p.numel() for p in model.parameters()):,}")
         # drop model from trainer_args if present
         state["trainer_args"].pop("model", None)
-        trainer = Trainer(model=model, **state["trainer_args"])
+        trainer = cls(model=model, **state["trainer_args"])
         trainer.model.to(trainer.device)
         trainer.optimizer.load_state_dict(state["optimizer"])
         trainer.scheduler.load_state_dict(state["scheduler"])
@@ -791,6 +792,8 @@ def forward(
             out["s_MAE_size"] = stress_target.shape[0]
 
         # Mag
+        print(f"{list(prediction)=}")
+        print(f"{list(targets)=}")
         if "m" in self.target_str:
             mag_preds, mag_targets = [], []
             m_mae_size = 0

examples/make_graphs.py

@@ -29,10 +29,11 @@ def make_graphs(
     """Make graphs from a StructureJsonData dataset.
 
     Args:
-        data (StructureJsonData): a StructureJsonData
-        graph_dir (str): a directory to save the graphs
-        train_ratio (float): train ratio
-        val_ratio (float): val ratio
+        data (StructureJsonData | StructureData): Input structures to convert to graphs.
+        graph_dir (str): a directory to save the graphs and labels.
+        train_ratio (float): train ratio. Default = 0.8
+        val_ratio (float): val ratio. Default = 0.1. The test ratio is
+            1 - train_ratio - val_ratio
     """
     os.makedirs(graph_dir, exist_ok=True)
     random.shuffle(data.keys)

pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "nvidia-ml-py3>=7.352.0",
     "pymatgen>=2023.10.11",
     "torch>=1.11.0",
+    "typing-extensions>=4.12",
 ]
 classifiers = [
     "Intended Audience :: Science/Research",