fix: efficient ad model_size str fixes (#2159)

* fix: model str conversion

Signed-off-by: Lukas Hennies <lukas.hennies@anticipate.ml>

* fix: str support for model_size param in efficient ad

Signed-off-by: Lukas Hennies <lukas.hennies@anticipate.ml>

* chore: update type hints for efficient_ad init

also pre-commit
Signed-off-by: Lukas Hennies <lukas.hennies@anticipate.ml>

---------

Signed-off-by: Lukas Hennies <lukas.hennies@anticipate.ml>
Co-authored-by: Samet Akcay <samet.akcay@intel.com>

src/anomalib/models/image/efficient_ad/lightning_model.py

@@ -63,7 +63,7 @@ def __init__(
         self,
         imagenet_dir: Path | str = "./datasets/imagenette",
         teacher_out_channels: int = 384,
-        model_size: EfficientAdModelSize = EfficientAdModelSize.S,
+        model_size: EfficientAdModelSize | str = EfficientAdModelSize.S,
         lr: float = 0.0001,
         weight_decay: float = 0.00001,
         padding: bool = False,
@@ -72,24 +72,27 @@ def __init__(
         super().__init__()
 
         self.imagenet_dir = Path(imagenet_dir)
-        self.model_size = model_size
+        if not isinstance(model_size, EfficientAdModelSize):
+            model_size = EfficientAdModelSize(model_size)
+        self.model_size: EfficientAdModelSize = model_size
         self.model: EfficientAdModel = EfficientAdModel(
             teacher_out_channels=teacher_out_channels,
             model_size=model_size,
             padding=padding,
             pad_maps=pad_maps,
         )
-        self.batch_size = 1  # imagenet dataloader batch_size is 1 according to the paper
-        self.lr = lr
-        self.weight_decay = weight_decay
+        self.batch_size: int = 1  # imagenet dataloader batch_size is 1 according to the paper
+        self.lr: float = lr
+        self.weight_decay: float = weight_decay
 
     def prepare_pretrained_model(self) -> None:
         """Prepare the pretrained teacher model."""
         pretrained_models_dir = Path("./pre_trained/")
         if not (pretrained_models_dir / "efficientad_pretrained_weights").is_dir():
             download_and_extract(pretrained_models_dir, WEIGHTS_DOWNLOAD_INFO)
+        model_size_str = self.model_size.value if isinstance(self.model_size, EfficientAdModelSize) else self.model_size
         teacher_path = (
-            pretrained_models_dir / "efficientad_pretrained_weights" / f"pretrained_teacher_{self.model_size.value}.pth"
+            pretrained_models_dir / "efficientad_pretrained_weights" / f"pretrained_teacher_{model_size_str}.pth"
         )
         logger.info(f"Load pretrained teacher model from {teacher_path}")
         self.model.teacher.load_state_dict(torch.load(teacher_path, map_location=torch.device(self.device)))