Removed Imagenet normalization, added check-up for train_batch_size

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

@@ -80,7 +80,7 @@ def __init__(
             padding=padding,
             pad_maps=pad_maps,
         )
-        self.batch_size = 1  # imagenet dataloader batch size is 1 according to the paper
+        self.batch_size = 1  # imagenet dataloader batch_size is 1 according to the paper
         self.lr = lr
         self.weight_decay = weight_decay
 
@@ -234,9 +234,18 @@ def configure_optimizers(self) -> torch.optim.Optimizer:
     def on_train_start(self) -> None:
         """Called before the first training epoch.
 
-        First sets up the pretrained teacher model, then prepares the imagenette data, and finally calculates or
-        loads the channel-wise mean and std of the training dataset and push to the model.
+        First check if EfficientAd-specific parameters are set correctly (train_batch_size of 1
+        and no Imagenet normalization in transforms), then sets up the pretrained teacher model,
+        then prepares the imagenette data, and finally calculates or loads
+        the channel-wise mean and std of the training dataset and push to the model.
         """
+        if self.trainer.datamodule.train_batch_size != 1:
+            msg = "train_batch_size for EfficientAd should be 1."
+            raise ValueError(msg)
+        if self._transform and any(isinstance(transform, Normalize) for transform in self._transform.transforms):
+            msg = "Transforms for EfficientAd should not contain Normalize."
+            raise ValueError(msg)
+
         sample = next(iter(self.trainer.train_dataloader))
         image_size = sample["image"].shape[-2:]
         self.prepare_pretrained_model()
@@ -311,11 +320,10 @@ def learning_type(self) -> LearningType:
         return LearningType.ONE_CLASS
 
     def configure_transforms(self, image_size: tuple[int, int] | None = None) -> Transform:
-        """Default transform for Padim."""
+        """Default transform for EfficientAd. Imagenet normalization applied in forward."""
         image_size = image_size or (256, 256)
         return Compose(
             [
                 Resize(image_size, antialias=True),
-                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
             ],
         )