cleanup docstrings

Signed-off-by: Alfie Roddan <228966941+alfieroddanintel@users.noreply.github.com>

Author: alfieroddanintel

src/anomalib/models/image/anomaly_dino/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2025 Intel Corporation
+# Copyright (C) 2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 """AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2.
@@ -15,33 +15,32 @@
     >>> from anomalib.data import MVTecAD
     >>> from anomalib.models.image.anomaly_dino.lightning_model import AnomalyDINO
     >>> from anomalib.engine import Engine
-    >>>
+
     >>> MVTEC_CATEGORIES = [
     ...     "hazelnut", "grid", "carpet", "bottle", "cable", "capsule", "leather",
     ...     "metal_nut", "pill", "screw", "tile", "toothbrush", "transistor", "wood", "zipper"
     ... ]
     >>> MASKED_CATEGORIES = ["capsule", "hazelnut", "pill", "screw", "toothbrush"]
-    >>>
+
     >>> for category in MVTEC_CATEGORIES:
     ...     mask = category in MASKED_CATEGORIES
     ...     print(f"--- Running category: {category} | masking={mask} ---")
-    ...
+
     ...     # Initialize data module
     ...     datamodule = MVTecAD(category=category)
-    ...
+
     ...     # Initialize model
     ...     model = AnomalyDINO(
     ...         num_neighbours=1,
     ...         encoder_name="dinov2_vit_small_14",
     ...         masking=mask,
     ...         coreset_subsampling=False,
     ...     )
-    ...
+
     ...     # Train and test
     ...     engine = Engine()
     ...     engine.fit(model=model, datamodule=datamodule)
     ...     engine.test(datamodule=datamodule)
-    >>>
     >>> print("All categories processed.")
 """
 

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

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2025 Intel Corporation
+# Copyright (C) 2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 """AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2.
@@ -15,33 +15,32 @@
     >>> from anomalib.data import MVTecAD
     >>> from anomalib.models.image.anomaly_dino.lightning_model import AnomalyDINO
     >>> from anomalib.engine import Engine
-    >>>
+
     >>> MVTEC_CATEGORIES = [
     ...     "hazelnut", "grid", "carpet", "bottle", "cable", "capsule", "leather",
     ...     "metal_nut", "pill", "screw", "tile", "toothbrush", "transistor", "wood", "zipper"
     ... ]
     >>> MASKED_CATEGORIES = ["capsule", "hazelnut", "pill", "screw", "toothbrush"]
-    >>>
+
     >>> for category in MVTEC_CATEGORIES:
     ...     mask = category in MASKED_CATEGORIES
     ...     print(f"--- Running category: {category} | masking={mask} ---")
-    ...
+
     ...     # Initialize data module
     ...     datamodule = MVTecAD(category=category)
-    ...
+
     ...     # Initialize model
     ...     model = AnomalyDINO(
     ...         num_neighbours=1,
     ...         encoder_name="dinov2_vit_small_14",
     ...         masking=mask,
     ...         coreset_subsampling=False,
     ...     )
-    ...
+
     ...     # Train and test
     ...     engine = Engine()
     ...     engine.fit(model=model, datamodule=datamodule)
     ...     engine.test(datamodule=datamodule)
-    >>>
     >>> print("All categories processed.")
 """
 
@@ -107,33 +106,33 @@ class AnomalyDINO(MemoryBankMixin, AnomalibModule):
         >>> from anomalib.data import MVTecAD
         >>> from anomalib.models.image.anomaly_dino.lightning_model import AnomalyDINO
         >>> from anomalib.engine import Engine
-        >>>
+
         >>> MVTEC_CATEGORIES = [
         ...     "hazelnut", "grid", "carpet", "bottle", "cable", "capsule", "leather",
         ...     "metal_nut", "pill", "screw", "tile", "toothbrush", "transistor", "wood", "zipper"
         ... ]
         >>> MASKED_CATEGORIES = ["capsule", "hazelnut", "pill", "screw", "toothbrush"]
-        >>>
+
         >>> for category in MVTEC_CATEGORIES:
         ...     mask = category in MASKED_CATEGORIES
         ...     print(f"--- Running category: {category} | masking={mask} ---")
-        ...
+
         ...     # Initialize data module
         ...     datamodule = MVTecAD(category=category)
-        ...
+
         ...     # Initialize model
         ...     model = AnomalyDINO(
         ...         num_neighbours=1,
         ...         encoder_name="dinov2_vit_small_14",
         ...         masking=mask,
         ...         coreset_subsampling=False,
         ...     )
-        ...
+
         ...     # Train and test
         ...     engine = Engine()
         ...     engine.fit(model=model, datamodule=datamodule)
         ...     engine.test(datamodule=datamodule)
-        >>>
+
         >>> print("All categories processed.")
 
     Notes:

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

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2025 Intel Corporation
+# Copyright (C) 2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 """PyTorch model implementation for AnomalyDINO.
@@ -75,7 +75,9 @@ def __init__(
         self.sampling_ratio = sampling_ratio
 
         # Load DINOv2 backbone
-        assert encoder_name.startswith("dinov2"), f"Encoder must be dinov2, got {encoder_name}"
+        if not encoder_name.startswith("dinov2"):
+            err_str = f"Encoder must be dinov2, got {encoder_name}"
+            raise ValueError(err_str)
         self.feature_encoder = load_dinov2_model(self.encoder_name)
         self.feature_encoder.eval()