fix(dinov2_loader): fixes comments

Author: rajeshgangireddy

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

@@ -185,8 +185,7 @@ def _load_weights(
 
         # Weights_only is set to True
         # See mitigation details in https://github.com/open-edge-platform/anomalib/pull/2729
-        # nosemgrep: trailofbits.python.pickles-in-pytorch.pickles-in-pytorch
-        state_dict = torch.load(weight_path, map_location="cpu", weights_only=True)  # nosec B614
+        state_dict = torch.load(weight_path, map_location="cpu", weights_only=True)  # nosec B614  # nosemgrep: trailofbits.python.pickles-in-pytorch.pickles-in-pytorch
         model.load_state_dict(state_dict, strict=False)
 
     def _get_weight_path(

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

@@ -15,7 +15,7 @@
 from torch import nn
 
 
-def drop_path(x: torch.torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
+def drop_path(x: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
     """Apply stochastic depth to an input tensor.
 
     Args:

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

@@ -88,7 +88,7 @@ class AnomalyDINO(MemoryBankMixin, AnomalibModule):
             to simulate occlusions or missing patches. Defaults to ``False``.
         coreset_subsampling (bool, optional): Whether to apply coreset subsampling
             to reduce the size of the memory bank. Defaults to ``False``.
-        sampling ratio(float, optional): If coreset subsampling, by what ratio
+        sampling_ratio (float, optional): If coreset subsampling, by what ratio
             should we subsample. Defaults to ``0.1``
         precision (str, optional): Precision type for model computations.
             Supported values are defined in :class:`PrecisionType`.

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

@@ -220,7 +220,7 @@ def forward(self, input_tensor: torch.Tensor) -> torch.Tensor | InferenceBatch:
                     * ``pred_score``: Image-level anomaly score ``(B, 1)``
                     * ``anomaly_map``: Pixel-level anomaly heatmap ``(B, 1, H, W)``
         """
-        # set precicion
+        # set precision
         input_tensor = input_tensor.type(self.memory_bank.dtype)
 
         # work out sizing

tests/unit/models/image/anomaly_dino/test_torch_model.py

@@ -1,7 +1,7 @@
 # Copyright (C) 2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-"""Unit tests for the WinCLIP torch model."""
+"""Unit tests for the AnomalyDINO torch model."""
 
 import numpy as np
 import pytest