add from_name back

Signed-off-by: Alfie Roddan <[email protected]>

Author: alfieroddanintel

src/anomalib/models/components/dinov2/dinov2_loader.py

@@ -148,7 +148,7 @@ def create_model(self, model_type: str, architecture: str, patch_size: int) -> t
             model_kwargs["num_register_tokens"] = 4
 
         # If user supplied a custom ViT module, use it
-        module = self.vit_factory or MODEL_FACTORIES[model_type]
+        module = self.vit_factory if self.vit_factory is not None else MODEL_FACTORIES[model_type]
 
         ctor = getattr(module, f"vit_{architecture}", None)
         if ctor is None:

src/anomalib/models/image/dinomaly/components/vision_transformer.py

@@ -19,10 +19,11 @@
 from functools import partial
 
 import torch
+from timm.layers.patch_embed import PatchEmbed
 from torch import nn
 from torch.nn.init import trunc_normal_
 
-from anomalib.models.components.dinov2.layers import MemEffAttention, PatchEmbed
+from anomalib.models.components.dinov2.layers import MemEffAttention
 from anomalib.models.image.dinomaly.components.layers import Block, DinomalyMLP
 
 logger = logging.getLogger("dinov2")

src/anomalib/models/image/dinomaly/torch_model.py

@@ -24,9 +24,7 @@
 from anomalib.models.components import GaussianBlur2d
 from anomalib.models.components.dinov2 import DinoV2Loader
 from anomalib.models.image.dinomaly.components import CosineHardMiningLoss, DinomalyMLP, LinearAttention
-from anomalib.models.image.dinomaly.components.vision_transformer import (
-    DinoVisionTransformer as DinomalyVisionTransformer,
-)
+from anomalib.models.image.dinomaly.components import vision_transformer as dinomaly_vision_transformer
 
 # Encoder architecture configurations for DINOv2 models.
 # The target layers are the
@@ -122,8 +120,8 @@ def __init__(
 
         self.encoder_name = encoder_name
         encoder = DinoV2Loader(
-            vit_factory=DinomalyVisionTransformer,
-        ).from_name(encoder_name)
+            vit_factory=dinomaly_vision_transformer,
+        ).load(encoder_name)
 
         # Extract architecture configuration based on the model name
         arch_config = self._get_architecture_config(encoder_name, target_layers)