chore(ci): add python 3.13 compatibility

Signed-off-by: Rickypanta0 <[email protected]>

Author: Rickypanta0

.github/workflows/pr.yaml

@@ -99,7 +99,6 @@ jobs:
     with:
       test-type: "integration"
       runner: "self-hosted"
-      timeout: 60
       enable-cache: "false"
     secrets:
       codecov-token: ${{ secrets.CODECOV_TOKEN }}

pyproject.toml

@@ -22,6 +22,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Topic :: Software Development :: Libraries",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
@@ -100,19 +101,11 @@ test = [
 ]
 # PyTorch dependency groups
 cpu = [
-    "torch>=2.4.0,<=2.8.0",
-    "torchvision>=0.19.0",
-]
-cu118 = [
-    "torch>=2.4.0,<=2.8.0",
-    "torchvision>=0.19.0",
-]
-cu121 = [
-    "torch>=2.4.0,<=2.8.0",
+    "torch>=2.4.0",
     "torchvision>=0.19.0",
 ]
 cu124 = [
-    "torch>=2.4.0,<=2.8.0",
+    "torch>=2.4.0",
     "torchvision>=0.19.0",
 ]
 cu130 = [
@@ -159,8 +152,6 @@ managed = true
 conflicts = [
     [
         { extra = "cpu" },
-        { extra = "cu118" },
-        { extra = "cu121" },
         { extra = "cu124" },
         { extra = "cu130" },
         { extra = "rocm" },
@@ -174,16 +165,6 @@ name = "pytorch-cpu"
 url = "https://download.pytorch.org/whl/cpu"
 explicit = true
 
-[[tool.uv.index]]
-name = "pytorch-cu118"
-url = "https://download.pytorch.org/whl/cu118"
-explicit = true
-
-[[tool.uv.index]]
-name = "pytorch-cu121"
-url = "https://download.pytorch.org/whl/cu121"
-explicit = true
-
 [[tool.uv.index]]
 name = "pytorch-cu124"
 url = "https://download.pytorch.org/whl/cu124"
@@ -208,17 +189,13 @@ explicit = true
 [tool.uv.sources]
 torch = [
     { index = "pytorch-cpu", extra = "cpu" },
-    { index = "pytorch-cu118", extra = "cu118" },
-    { index = "pytorch-cu121", extra = "cu121" },
     { index = "pytorch-cu124", extra = "cu124" },
     { index = "pytorch-cu130", extra = "cu130" },
     { index = "pytorch-rocm61", extra = "rocm" },
     { index = "pytorch-xpu", extra = "xpu" },
 ]
 torchvision = [
     { index = "pytorch-cpu", extra = "cpu" },
-    { index = "pytorch-cu118", extra = "cu118" },
-    { index = "pytorch-cu121", extra = "cu121" },
     { index = "pytorch-cu124", extra = "cu124" },
     { index = "pytorch-cu130", extra = "cu130" },
     { index = "pytorch-rocm61", extra = "rocm" },
@@ -235,9 +212,46 @@ pytorch-triton-xpu = [
 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
 # RUFF CONFIGURATION                                                          #
 [tool.ruff]
+# Exclude a variety of commonly ignored directories.
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+]
+
+# Same as Black.
+line-length = 120
+
+# Assume Python 3.10.
+target-version = "py310"
+
+# Allow imports relative to the "src" and "tests" directories.
+src = ["src", "tests"]
+
+[tool.ruff.lint]
 # Enable preview features
 preview = true
 
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
 # Enable rules
 select = [
     "F",    # Pyflakes (`F`)
@@ -286,8 +300,6 @@ select = [
     "NPY",  # NumPy-specific rules (`NPY`)
     "PERF", # Perflint (`PERF`)
     "RUF",  # Ruff-specific rules (`RUF`)
-    # "FURB", # refurb (`FURB`) - ERROR: Unknown rule selector: `FURB`
-    # "LOG",  # flake8-logging (`LOG`) - ERROR: Unknown rule selector: `LOG`
 ]
 
 ignore = [
@@ -346,57 +358,21 @@ ignore = [
 fixable = ["ALL"]
 unfixable = []
 
-# Exclude a variety of commonly ignored directories.
-exclude = [
-    ".bzr",
-    ".direnv",
-    ".eggs",
-    ".git",
-    ".hg",
-    ".mypy_cache",
-    ".nox",
-    ".pants.d",
-    ".pytype",
-    ".ruff_cache",
-    ".svn",
-    ".tox",
-    ".venv",
-    "__pypackages__",
-    "_build",
-    "buck-out",
-    "build",
-    "dist",
-    "node_modules",
-    "venv",
-]
-
-# Same as Black.
-line-length = 120
-
-# Allow unused variables when underscore-prefixed.
-dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
-
-# Assume Python 3.10.
-target-version = "py310"
-
-# Allow imports relative to the "src" and "tests" directories.
-src = ["src", "tests"]
-
-[tool.ruff.mccabe]
+[tool.ruff.lint.mccabe]
 # Unlike Flake8, default to a complexity level of 10.
 max-complexity = 15
 
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "google"
 
-[tool.ruff.flake8-copyright]
+[tool.ruff.lint.flake8-copyright]
 notice-rgx = """
 # Copyright \\(C\\) (\\d{4}(-\\d{4})?) Intel Corporation
 # SPDX-License-Identifier: Apache-2\\.0
 """
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "examples/notebooks/**/*" = ["CPY001"]
 
 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #

src/anomalib/data/dataclasses/torch/base.py

@@ -20,8 +20,6 @@
 
 from anomalib.data.dataclasses.generic import ImageT, _GenericBatch, _GenericItem
 
-NumpyT = TypeVar("NumpyT")
-
 
 class InferenceBatch(NamedTuple):
     """Batch for use in torch and inference models.
@@ -43,6 +41,9 @@ class InferenceBatch(NamedTuple):
     pred_mask: torch.Tensor | None = None
 
 
+NumpyT = TypeVar("NumpyT")
+
+
 @dataclass
 class ToNumpyMixin(Generic[NumpyT]):
     """Mixin for converting torch-based dataclasses to numpy.

src/anomalib/data/datasets/image/datumaro.py

@@ -80,13 +80,15 @@ def make_datumaro_dataset(
         image_path = Path(root) / "images" / "default" / item["image"]["path"]
         label_index = item["annotations"][0]["label_id"]
         label = categories[label_index]
-        samples.append({
-            "image_path": str(image_path),
-            "label": label,
-            "label_index": label_index,
-            "split": None,
-            "mask_path": "",  # mask is provided in annotation file
-        })
+        samples.append(
+            {
+                "image_path": str(image_path),
+                "label": label,
+                "label_index": label_index,
+                "split": None,
+                "mask_path": "",  # mask is provided in annotation file
+            },
+        )
     samples_df = pd.DataFrame(
         samples,
         columns=["image_path", "label", "label_index", "split", "mask_path"],

src/anomalib/engine/engine.py

@@ -365,6 +365,7 @@ def fit(
         val_dataloaders: EVAL_DATALOADERS | None = None,
         datamodule: AnomalibDataModule | None = None,
         ckpt_path: str | Path | None = None,
+        **kwargs,  # noqa: ARG002
     ) -> None:
         """Fit the model using the trainer.
 
@@ -379,6 +380,7 @@ def fit(
                 Defaults to None.
             ckpt_path (str | None, optional): Checkpoint path. If provided, the model will be loaded from this path.
                 Defaults to None.
+            **kwargs: Additional arguments passed to PyTorch Lightning Trainer's fit method.
 
         CLI Usage:
             1. you can pick a model, and you can run through the MVTec dataset.
@@ -415,7 +417,14 @@ def fit(
                 weights_only=False,
             )
         else:
-            self.trainer.fit(model, train_dataloaders, val_dataloaders, datamodule, ckpt_path, weights_only=False)
+            self.trainer.fit(
+                model,
+                train_dataloaders,
+                val_dataloaders,
+                datamodule,
+                ckpt_path,
+                weights_only=False,
+            )
 
     def validate(
         self,
@@ -424,6 +433,7 @@ def validate(
         ckpt_path: str | Path | None = None,
         verbose: bool = True,
         datamodule: AnomalibDataModule | None = None,
+        **kwargs,  # noqa: ARG002
     ) -> _EVALUATE_OUTPUT | None:
         """Validate the model using the trainer.
 
@@ -441,6 +451,7 @@ def validate(
                 AnomalibDataModule` that defines the
                 :class:`~lightning.pytorch.core.hooks.DataHooks.val_dataloader` hook.
                 Defaults to None.
+            **kwargs: Additional arguments passed to PyTorch Lightning Trainer's validate method.
 
         Returns:
             _EVALUATE_OUTPUT | None: Validation results.
@@ -472,6 +483,7 @@ def test(
         ckpt_path: str | Path | None = None,
         verbose: bool = True,
         datamodule: AnomalibDataModule | None = None,
+        **kwargs,  # noqa: ARG002
     ) -> _EVALUATE_OUTPUT:
         """Test the model using the trainer.
 
@@ -497,6 +509,7 @@ def test(
                 A :class:`~lightning.pytorch.core.datamodule.AnomalibDataModule` that defines
                 the :class:`~lightning.pytorch.core.hooks.DataHooks.test_dataloader` hook.
                 Defaults to None.
+            **kwargs: Additional arguments passed to PyTorch Lightning Trainer's test method.
 
         Returns:
             _EVALUATE_OUTPUT: A List of dictionaries containing the test results. 1 dict per dataloader.
@@ -556,7 +569,7 @@ def test(
 
         if self._should_run_validation(model or self.model, ckpt_path):
             logger.info("Running validation before testing to collect normalization metrics and/or thresholds.")
-            self.trainer.validate(model, dataloaders, None, verbose=False, datamodule=datamodule)
+            self.trainer.validate(model, dataloaders, None, verbose=False, datamodule=datamodule, weights_only=False)
         return self.trainer.test(model, dataloaders, ckpt_path, verbose, datamodule, weights_only=False)
 
     def predict(
@@ -568,6 +581,7 @@ def predict(
         return_predictions: bool | None = None,
         ckpt_path: str | Path | None = None,
         data_path: str | Path | None = None,
+        **kwargs,  # noqa: ARG002
     ) -> _PREDICT_OUTPUT | None:
         """Predict using the model using the trainer.
 
@@ -601,6 +615,7 @@ def predict(
             data_path (str | Path | None):
                 Path to the image or folder containing images to generate predictions for.
                 Defaults to None.
+            **kwargs: Additional arguments passed to PyTorch Lightning Trainer's predict method.
 
         Returns:
             _PREDICT_OUTPUT | None: Predictions.
@@ -747,6 +762,7 @@ def export(
         ov_kwargs: dict[str, Any] | None = None,
         onnx_kwargs: dict[str, Any] | None = None,
         ckpt_path: str | Path | None = None,
+        **kwargs,  # noqa: ARG002
     ) -> Path | None:
         r"""Export the model in PyTorch, ONNX or OpenVINO format.
 
@@ -785,6 +801,7 @@ def export(
                 See https://pytorch.org/docs/stable/onnx.html#torch.onnx.export for details.
                 Defaults to ``None``.
             ckpt_path (str | Path | None): Checkpoint path. If provided, the model will be loaded from this path.
+            **kwargs: Additional arguments.
 
         Returns:
             Path: Path to the exported model.

src/anomalib/metrics/threshold/f1_adaptive_threshold.py

@@ -48,7 +48,7 @@
 
 
 @contextmanager
-def handle_mac(metric: "_F1AdaptiveThreshold") -> Generator[None, None, None]:
+def handle_mac(metric: "_F1AdaptiveThreshold") -> Generator[None]:
     """Temporarily move tensors to CPU on macOS/MPS and restore after.
 
     This context manager checks whether the provided metric instance has

src/anomalib/models/components/base/anomalib_module.py

@@ -328,10 +328,12 @@ def configure_pre_processor(image_size: tuple[int, int] | None = None) -> PrePro
         """
         image_size = image_size or (256, 256)
         return PreProcessor(
-            transform=Compose([
-                Resize(image_size, antialias=True),
-                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-            ]),
+            transform=Compose(
+                [
+                    Resize(image_size, antialias=True),
+                    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+                ],
+            ),
         )
 
     def configure_post_processor(self) -> PostProcessor | None: