chore(ci): enable Python 3.13 support

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

Author: Rickypanta0

.github/workflows/pre_merge.yml

@@ -30,7 +30,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6
         with:
-          python-version: "3.10"
+          python-version: "3.13"
       - name: Install Tox
         run: pip install tox
       - name: Code quality checks
@@ -43,8 +43,8 @@ jobs:
       max-parallel: 1
       matrix:
         include:
-          - python-version: "3.10"
-            tox-env: "py310"
+          - python-version: "3.13"
+            tox-env: "py313"
     steps:
       - name: Checkout repository
         uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5

docs/source/markdown/guides/developer/contributing.md

@@ -18,7 +18,7 @@ Set up your development environment to start contributing. This involves install
 1. Create and activate a new Conda environment:
 
    ```bash
-   conda create -n anomalib_dev python=3.10
+   conda create -n anomalib_dev python=3.13
    conda activate anomalib_dev
    ```
 

pyproject.toml

@@ -9,7 +9,7 @@ name = "anomalib"
 dynamic = ["version"]
 readme = "README.md"
 description = "anomalib - Anomaly Detection Library"
-requires-python = ">=3.10"
+requires-python = ">=3.13"
 license = { file = "LICENSE" }
 authors = [{ name = "Intel OpenVINO" }]
 
@@ -18,10 +18,7 @@ classifiers = [
     "Intended Audience :: Developers",
     "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python",
-    "Programming Language :: Python :: 3",
-    "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 +97,19 @@ test = [
 ]
 # PyTorch dependency groups
 cpu = [
-    "torch>=2.4.0,<=2.8.0",
+    "torch>=2.4.0",
     "torchvision>=0.19.0",
 ]
 cu118 = [
-    "torch>=2.4.0,<=2.8.0",
-    "torchvision>=0.19.0",
+    "torch>=2.4.0,<=2.5.1 ; python_version < '3.13'",
+    "torchvision>=0.19.0 ; python_version < '3.13'",
 ]
 cu121 = [
-    "torch>=2.4.0,<=2.8.0",
-    "torchvision>=0.19.0",
+    "torch>=2.4.0 ; python_version < '3.13'",
+    "torchvision>=0.19.0 ; python_version < '3.13'",
 ]
 cu124 = [
-    "torch>=2.4.0,<=2.8.0",
+    "torch>=2.4.0",
     "torchvision>=0.19.0",
 ]
 cu130 = [
@@ -238,6 +235,44 @@ pytorch-triton-xpu = [
 # Enable preview features
 preview = true
 
+# 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 3.13.
+target-version = "py313"
+
+# Allow imports relative to the "src" and "tests" directories.
+src = ["src", "tests"]
+
+[tool.ruff.lint]
+
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
 # Enable rules
 select = [
     "F",    # Pyflakes (`F`)
@@ -292,6 +327,7 @@ select = [
 
 ignore = [
     # pydocstyle
+    "COM812",
     "D107", # Missing docstring in __init__
 
     # pylint
@@ -346,57 +382,21 @@ 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/__init__.py

@@ -32,12 +32,12 @@
     with a focus on reproducibility and ease of use.
 """
 
-from enum import Enum
+from enum import StrEnum
 
 __version__ = "2.3.0dev"
 
 
-class LearningType(str, Enum):
+class LearningType(StrEnum):
     """Learning type defining how the model learns from the dataset samples.
 
     This enum defines the different learning paradigms supported by anomalib models:
@@ -62,7 +62,7 @@ class LearningType(str, Enum):
     FEW_SHOT = "few_shot"
 
 
-class TaskType(str, Enum):
+class TaskType(StrEnum):
     """Task type defining the model's prediction output format.
 
     This enum defines the different task types supported by anomalib models:
@@ -88,7 +88,7 @@ class TaskType(str, Enum):
     SEGMENTATION = "segmentation"
 
 
-class PrecisionType(str, Enum):
+class PrecisionType(StrEnum):
     """Precision type defining the numerical precision used in model computations.
 
     This enum defines the different precision types supported by anomalib models:

src/anomalib/data/dataclasses/generic.py

@@ -53,10 +53,9 @@
 
 
 Instance = TypeVar("Instance")
-Value = TypeVar("Value")
 
 
-class FieldDescriptor(Generic[Value]):
+class FieldDescriptor[Value]:
     """Descriptor for Anomalib's dataclass fields.
 
     Using a descriptor ensures that the values of dataclass fields can be
@@ -160,7 +159,7 @@ def is_optional(self, owner: type[Instance]) -> bool:
 
 
 @dataclass
-class _InputFields(Generic[T, ImageT, MaskT, PathT], ABC):
+class _InputFields(Generic[T, ImageT, MaskT, PathT], ABC):  # noqa: UP046
     """Generic dataclass that defines the standard input fields for Anomalib.
 
     This abstract base class provides a structure for input data used in Anomalib.
@@ -258,7 +257,7 @@ def validate_gt_label(gt_label: T) -> T | None:
 
 
 @dataclass
-class _ImageInputFields(Generic[PathT], ABC):
+class _ImageInputFields(Generic[PathT], ABC):  # noqa: UP046
     """Generic dataclass for image-specific input fields in Anomalib.
 
     This class extends standard input fields with an ``image_path`` attribute for
@@ -295,7 +294,7 @@ def validate_image_path(image_path: PathT) -> PathT | None:
 
 
 @dataclass
-class _VideoInputFields(Generic[T, ImageT, MaskT, PathT], ABC):
+class _VideoInputFields(Generic[T, ImageT, MaskT, PathT], ABC):  # noqa: UP046
     """Generic dataclass that defines the video input fields for Anomalib.
 
     This class extends standard input fields with attributes specific to
@@ -411,7 +410,7 @@ def validate_last_frame(last_frame: T) -> T | None:
 
 
 @dataclass
-class _DepthInputFields(Generic[T, PathT], _ImageInputFields[PathT], ABC):
+class _DepthInputFields(Generic[T, PathT], _ImageInputFields[PathT], ABC):  # noqa: UP046
     """Generic dataclass that defines the depth input fields for Anomalib.
 
     This class extends standard input fields with depth-specific attributes for
@@ -473,7 +472,7 @@ def validate_depth_path(depth_path: PathT) -> PathT | None:
 
 
 @dataclass
-class _OutputFields(Generic[T, MaskT, PathT], ABC):
+class _OutputFields(Generic[T, MaskT, PathT], ABC):  # noqa: UP046
     """Generic dataclass that defines the standard output fields for Anomalib.
 
     This class defines the standard output fields used in Anomalib, including
@@ -640,7 +639,7 @@ def update(self, in_place: bool = True, **changes) -> Any:  # noqa: ANN401
 @dataclass
 class _GenericItem(
     UpdateMixin,
-    Generic[T, ImageT, MaskT, PathT],
+    Generic[T, ImageT, MaskT, PathT],  # noqa: UP046
     _OutputFields[T, MaskT, PathT],
     _InputFields[T, ImageT, MaskT, PathT],
 ):
@@ -669,7 +668,7 @@ class _GenericItem(
 @dataclass
 class _GenericBatch(
     UpdateMixin,
-    Generic[T, ImageT, MaskT, PathT],
+    Generic[T, ImageT, MaskT, PathT],  # noqa: UP046
     _OutputFields[T, MaskT, PathT],
     _InputFields[T, ImageT, MaskT, PathT],
 ):
@@ -696,7 +695,7 @@ class _GenericBatch(
 
 
 @dataclass
-class BatchIterateMixin(Generic[ItemT]):
+class BatchIterateMixin(Generic[ItemT]):  # noqa: UP046
     """Mixin class for iterating over batches of items in Anomalib datasets.
 
     This class provides functionality to iterate over individual items within a

src/anomalib/data/dataclasses/numpy/depth.py

@@ -26,7 +26,9 @@
     (224, 224)
 """
 
+from collections.abc import Callable
 from dataclasses import dataclass
+from typing import ClassVar
 
 import numpy as np
 
@@ -65,6 +67,7 @@ class NumpyDepthItem(
     """
 
 
+@dataclass
 class NumpyDepthBatch(
     BatchIterateMixin[NumpyDepthItem],
     NumpyDepthBatchValidator,
@@ -87,4 +90,4 @@ class NumpyDepthBatch(
     tensor-like fields.
     """
 
-    item_class = NumpyDepthItem
+    item_class: ClassVar[Callable] = NumpyDepthItem

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

@@ -13,15 +13,13 @@
 
 from collections.abc import Callable
 from dataclasses import dataclass, fields
-from typing import Any, ClassVar, Generic, NamedTuple, TypeVar
+from typing import Any, ClassVar, Generic, NamedTuple
 
 import torch
 from torchvision.tv_tensors import Mask
 
 from anomalib.data.dataclasses.generic import ImageT, _GenericBatch, _GenericItem
 
-NumpyT = TypeVar("NumpyT")
-
 
 class InferenceBatch(NamedTuple):
     """Batch for use in torch and inference models.
@@ -44,7 +42,7 @@ class InferenceBatch(NamedTuple):
 
 
 @dataclass
-class ToNumpyMixin(Generic[NumpyT]):
+class ToNumpyMixin[NumpyT]:
     """Mixin for converting torch-based dataclasses to numpy.
 
     This mixin provides functionality to convert PyTorch tensor data to numpy
@@ -103,7 +101,7 @@ def to_numpy(self) -> NumpyT:
 
 
 @dataclass
-class DatasetItem(Generic[ImageT], _GenericItem[torch.Tensor, ImageT, Mask, str]):
+class DatasetItem(Generic[ImageT], _GenericItem[torch.Tensor, ImageT, Mask, str]):  # noqa: UP046
     """Base dataclass for individual items in Anomalib datasets using PyTorch.
 
     This class extends the generic ``_GenericItem`` class to provide a
@@ -122,7 +120,7 @@ class DatasetItem(Generic[ImageT], _GenericItem[torch.Tensor, ImageT, Mask, str]
 
 
 @dataclass
-class Batch(Generic[ImageT], _GenericBatch[torch.Tensor, ImageT, Mask, list[str]]):
+class Batch(Generic[ImageT], _GenericBatch[torch.Tensor, ImageT, Mask, list[str]]):  # noqa: UP046
     """Base dataclass for batches of items in Anomalib datasets using PyTorch.
 
     This class extends the generic ``_GenericBatch`` class to provide a

src/anomalib/data/datamodules/depth/__init__.py

@@ -3,14 +3,14 @@
 
 """Anomalib Depth Data Modules."""
 
-from enum import Enum
+from enum import StrEnum
 
 from .adam_3d import ADAM3D
 from .folder_3d import Folder3D
 from .mvtec_3d import MVTec3D
 
 
-class DepthDataFormat(str, Enum):
+class DepthDataFormat(StrEnum):
     """Supported Depth Dataset Types."""
 
     MVTEC_3D = "mvtec_3d"