Ignore copyrights in notebooks

notebooks/000_getting_started/001_getting_started.ipynb

@@ -168,7 +168,7 @@
     "from anomalib import TaskType\n",
     "from anomalib.data import MVTec\n",
     "from anomalib.data.utils import read_image\n",
-    "from anomalib.deploy import OpenVINOInferencer, ExportType\n",
+    "from anomalib.deploy import ExportType, OpenVINOInferencer\n",
     "from anomalib.engine import Engine\n",
     "from anomalib.models import Padim"
    ]

notebooks/500_use_cases/501_dobot/501a_training_a_model_with_cubes_from_a_robotic_arm.ipynb

@@ -141,8 +141,8 @@
     }
    ],
    "source": [
-    "from anomalib.data import Folder\n",
     "from anomalib import TaskType\n",
+    "from anomalib.data import Folder\n",
     "\n",
     "datamodule = Folder(\n",
     "    name=\"cubes\",\n",
@@ -646,9 +646,10 @@
     }
    ],
    "source": [
-    "from anomalib.utils.visualization.image import ImageVisualizer, VisualizationMode\n",
     "from PIL import Image\n",
     "\n",
+    "from anomalib.utils.visualization.image import ImageVisualizer, VisualizationMode\n",
+    "\n",
     "visualizer = ImageVisualizer(mode=VisualizationMode.FULL, task=TaskType.CLASSIFICATION)\n",
     "output_image = visualizer.visualize_image(predictions)\n",
     "Image.fromarray(output_image)"

notebooks/500_use_cases/501_dobot/501b_inference_with_a_robotic_arm.ipynb

@@ -58,18 +58,20 @@
     "\n",
     "# Anomalib imports\n",
     "from __future__ import annotations\n",
-    "from typing import TYPE_CHECKING\n",
+    "\n",
     "import sys\n",
     "import time  # time library\n",
     "from datetime import datetime\n",
     "from pathlib import Path\n",
     "from threading import Thread\n",
+    "from typing import TYPE_CHECKING\n",
     "\n",
     "if TYPE_CHECKING:\n",
     "    import numpy as np\n",
     "\n",
     "# importing required libraries\n",
     "import cv2  # OpenCV library\n",
+    "\n",
     "from anomalib.deploy import OpenVINOInferencer"
    ]
   },

notebooks/600_loggers/601_mlflow_logging.ipynb

@@ -129,7 +129,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!mlflow server"
+    "# !mlflow server"
    ]
   },
   {
@@ -175,15 +175,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from anomalib.data import MVTec\n",
+    "import warnings\n",
+    "\n",
+    "from lightning.pytorch.callbacks import EarlyStopping\n",
+    "\n",
     "from anomalib import TaskType\n",
     "from anomalib.callbacks.checkpoint import ModelCheckpoint\n",
-    "from lightning.pytorch.callbacks import EarlyStopping\n",
-    "from anomalib.models import Fastflow\n",
-    "from anomalib.loggers import AnomalibMLFlowLogger\n",
+    "from anomalib.data import MVTec\n",
     "from anomalib.engine import Engine\n",
+    "from anomalib.loggers import AnomalibMLFlowLogger\n",
+    "from anomalib.models import Fastflow\n",
     "\n",
-    "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -1044,7 +1046,7 @@
     "    pixel_metrics=\"AUROC\",\n",
     "    accelerator=\"auto\",\n",
     "    devices=1,\n",
-    "    logger=mlflow_logger, # Logger is set here\n",
+    "    logger=mlflow_logger,  # Logger is set here\n",
     "    **kwargs,\n",
     ")"
    ]

pyproject.toml

@@ -162,6 +162,9 @@ lint.ignore = [
     "PLR0915", # Too many statements
 
     # NOTE: Disable the following rules for now.
+    "A004", # import is shadowing a Python built-in
+    "A005", # Module is shadowing a Python built-in
+    "B909", # Mutation to loop iterable during iteration
     "PLR6301", # could be a function, class method or static method
     "PLW1514", # Add explicit encoding argument
     "PLR6201", # Convert to set
@@ -182,7 +185,6 @@ lint.ignore = [
 
     "RUF021", # Parenthesize the `and` subexpression
     "RUF022", # Apply an isort-style sorting to '__all__'
-    "RUF025", # Replace with `dict.fromkeys(iterable, value)`)
     "S404", # `subprocess` module is possibly insecure
     # End of disable rules
 
@@ -259,6 +261,9 @@ notice-rgx = """
 # SPDX-License-Identifier: Apache-2\\.0
 """
 
+[tool.ruff.lint.per-file-ignores]
+"notebooks/**/*" = ["CPY001"]
+
 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
 # MYPY CONFIGURATION.                                                         #
 [tool.mypy]

src/anomalib/callbacks/timer.py

@@ -105,5 +105,5 @@ def on_test_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
             else:
                 test_data_loader = trainer.test_dataloaders[0]
             output += f"(batch_size={test_data_loader.batch_size})"
-        output += f" : {self.num_images/testing_time} FPS"
+        output += f" : {self.num_images / testing_time} FPS"
         logger.info(output)

src/anomalib/models/video/ai_vad/clip/clip.py

@@ -104,15 +104,13 @@ def _convert_image_to_rgb(image):
 
 
 def _transform(n_px):
-    return Compose(
-        [
-            Resize(n_px, interpolation=BICUBIC),
-            CenterCrop(n_px),
-            _convert_image_to_rgb,
-            ToTensor(),
-            Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
-        ]
-    )
+    return Compose([
+        Resize(n_px, interpolation=BICUBIC),
+        CenterCrop(n_px),
+        _convert_image_to_rgb,
+        ToTensor(),
+        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+    ])
 
 
 def available_models() -> List[str]:

src/anomalib/models/video/ai_vad/clip/model.py

@@ -45,13 +45,11 @@ def __init__(self, inplanes, planes, stride=1):
         if stride > 1 or inplanes != planes * Bottleneck.expansion:
             # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
             self.downsample = nn.Sequential(
-                OrderedDict(
-                    [
-                        ("-1", nn.AvgPool2d(stride)),
-                        ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
-                        ("1", nn.BatchNorm2d(planes * self.expansion)),
-                    ]
-                )
+                OrderedDict([
+                    ("-1", nn.AvgPool2d(stride)),
+                    ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
+                    ("1", nn.BatchNorm2d(planes * self.expansion)),
+                ])
             )
 
     def forward(self, x: torch.Tensor):
@@ -192,13 +190,11 @@ def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
         self.attn = nn.MultiheadAttention(d_model, n_head)
         self.ln_1 = LayerNorm(d_model)
         self.mlp = nn.Sequential(
-            OrderedDict(
-                [
-                    ("c_fc", nn.Linear(d_model, d_model * 4)),
-                    ("gelu", QuickGELU()),
-                    ("c_proj", nn.Linear(d_model * 4, d_model)),
-                ]
-            )
+            OrderedDict([
+                ("c_fc", nn.Linear(d_model, d_model * 4)),
+                ("gelu", QuickGELU()),
+                ("c_proj", nn.Linear(d_model * 4, d_model)),
+            ])
         )
         self.ln_2 = LayerNorm(d_model)
         self.attn_mask = attn_mask
@@ -430,9 +426,9 @@ def build_model(state_dict: dict):
 
     if vit:
         vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len(
-            [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]
-        )
+        vision_layers = len([
+            k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")
+        ])
         vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
         grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
         image_resolution = vision_patch_size * grid_size

src/anomalib/pipelines/components/runners/parallel.py

@@ -60,7 +60,7 @@ def run(self, args: dict, prev_stage_results: PREV_STAGE_RESULT = None) -> GATHE
         """Run the job in parallel."""
         self.task_id = self.progress.add_task(self.generator.job_class.name, total=None)
         self.progress.start()
-        self.processes = {i: None for i in range(self.n_jobs)}
+        self.processes = dict.fromkeys(range(self.n_jobs))
 
         with ProcessPoolExecutor(max_workers=self.n_jobs, mp_context=multiprocessing.get_context("spawn")) as executor:
             for job in self.generator(args, prev_stage_results):

src/anomalib/utils/post_processing.py

@@ -35,7 +35,7 @@ def add_label(
     img_height, img_width, _ = image.shape
 
     font = cv2.FONT_HERSHEY_PLAIN
-    text = label_name if confidence is None else f"{label_name} ({confidence*100:.0f}%)"
+    text = label_name if confidence is None else f"{label_name} ({confidence * 100:.0f}%)"
 
     # get font sizing
     font_scale = min(img_width, img_height) * font_scale

tests/helpers/data.py

@@ -503,7 +503,7 @@ def _generate_dummy_avenue_dataset(
         train_path = self.dataset_root / train_dir
         train_path.mkdir(exist_ok=True, parents=True)
         for clip_idx in range(self.num_train):
-            clip_path = train_path / f"{clip_idx+1:02}.avi"
+            clip_path = train_path / f"{clip_idx + 1:02}.avi"
             frames, _ = self.video_generator.generate_video(length=32, first_label=LabelName.NORMAL, p_state_switch=0)
             fourcc = cv2.VideoWriter_fourcc("F", "M", "P", "4")
             writer = cv2.VideoWriter(str(clip_path), fourcc, 30, self.frame_shape)
@@ -517,8 +517,8 @@ def _generate_dummy_avenue_dataset(
         gt_path = self.dataset_root / ground_truth_dir / "testing_label_mask"
 
         for clip_idx in range(self.num_test):
-            clip_path = test_path / f"{clip_idx+1:02}.avi"
-            mask_path = gt_path / f"{clip_idx+1}_label"
+            clip_path = test_path / f"{clip_idx + 1:02}.avi"
+            mask_path = gt_path / f"{clip_idx + 1}_label"
             mask_path.mkdir(exist_ok=True, parents=True)
             frames, masks = self.video_generator.generate_video(length=32, p_state_switch=0.2)
             fourcc = cv2.VideoWriter_fourcc("F", "M", "P", "4")