update docstrings

Author: alfieroddanintel

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

@@ -6,6 +6,10 @@
 
 References:
 https://github.com/facebookresearch/dinov2/blob/main/dinov2/
+
+Classes:
+    DinoVisionTransformer: DINOv2 implementation.
+    DinoV2Loader: Loader class to support downloading and loading weights.
 """
 
 # vision transformer

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

@@ -1,15 +1,30 @@
 # Copyright (C) 2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-"""Loader for DINOv2 Vision Transformer models.
+"""Loading pre-trained DINOv2 Vision Transformer models.
 
-This module provides a simple interface for loading pre-trained DINOv2 Vision Transformer models for the
-Dinomaly anomaly detection framework.
+This module provides the :class:`DinoV2Loader` class for constructing and loading
+pre-trained DINOv2 Vision Transformer models used in the Dinomaly anomaly detection
+framework. It supports both standard DINOv2 models and register-token variants, and
+allows custom Vision Transformer factories to be supplied.
 
 Example:
-    model = DinoV2Loader.from_name("dinov2_vit_base_14")
-    model = DinoV2Loader.from_name("vit_base_14")
-    model = DinoV2Loader(vit_factory=my_custom_vit_module).load("dinov2reg_vit_base_14")
+    >>> from anomalib.models.components.dinov2 import DinoV2Loader
+    >>> loader = DinoV2Loader()
+    >>> model = loader.load("dinov2_vit_base_14")
+    >>> model = loader.load("vit_base_14")
+    >>> custom_loader = DinoV2Loader(vit_factory=my_custom_vit_module)
+    >>> model = custom_loader.load("dinov2reg_vit_base_14")
+
+The DINOv2 loader handles:
+
+- Parsing model names and validating architecture types
+- Constructing the appropriate Vision Transformer model
+- Locating or downloading the corresponding pre-trained weights
+- Supporting custom ViT implementations via a pluggable factory
+
+This enables a simple, unified interface for accessing DINOv2-based backbones in
+downstream anomaly detection tasks.
 """
 
 from __future__ import annotations

src/anomalib/models/components/dinov2/layers/__init__.py

@@ -4,7 +4,21 @@
 """Layers needed to build DINOv2.
 
 References:
-https://github.com/facebookresearch/dinov2/blob/main/dinov2/layers/__init__.py
+    https://github.com/facebookresearch/dinov2/blob/main/dinov2/layers/__init__.py
+
+Classes:
+    Attention: Standard multi-head self-attention layer used in Vision Transformers.
+    MemEffAttention: Memory-efficient variant of multi-head attention optimized for large inputs.
+    Block: Transformer block consisting of attention, MLP, residuals, and normalization layers.
+    CausalAttentionBlock: Transformer block with causal (autoregressive) attention masking.
+    DINOHead: Projection head used in DINO/DINOv2 for self-supervised feature learning.
+    DropPath: Implements stochastic depth, randomly dropping residual connections during training.
+    LayerScale: Applies learnable per-channel scaling to stabilize deep transformer training.
+    Mlp: Feedforward network used inside Vision Transformer blocks.
+    PatchEmbed: Converts image patches into token embeddings for Vision Transformer inputs.
+    SwiGLUFFN: SwiGLU-based feedforward network used in DINOv2 for improved expressiveness.
+    SwiGLUFFNAligned: Variant of SwiGLUFFN with tensor alignment optimizations.
+    SwiGLUFFNFused: Fused implementation of SwiGLUFFN for improved computational efficiency.
 """
 
 from .attention import Attention, MemEffAttention