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🔧 chore(ci): upgrade python 3.13 #3112

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1 change: 0 additions & 1 deletion .github/workflows/pr.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,6 @@ jobs:
with:
test-type: "integration"
runner: "self-hosted"
timeout: 60
enable-cache: "false"
secrets:
codecov-token: ${{ secrets.CODECOV_TOKEN }}
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112 changes: 44 additions & 68 deletions pyproject.toml
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Expand Up @@ -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",
]
Expand Down Expand Up @@ -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",
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Copilot AI Nov 23, 2025

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The removal of the upper bound <=2.8.0 for the cpu extra creates potential compatibility risks. If PyTorch 3.0+ introduces breaking changes, users could encounter issues. Consider retaining an upper bound (e.g., <3.0) or adding a comment explaining why the constraint was relaxed.

Suggested change
"torch>=2.4.0",
"torch>=2.4.0,<3.0",

Copilot uses AI. Check for mistakes.
"torchvision>=0.19.0",
]
cu124 = [
"torch>=2.4.0,<=2.8.0",
"torch>=2.4.0",
"torchvision>=0.19.0",
]
cu130 = [
Expand Down Expand Up @@ -159,8 +152,6 @@ managed = true
conflicts = [
[
{ extra = "cpu" },
{ extra = "cu118" },
{ extra = "cu121" },
{ extra = "cu124" },
{ extra = "cu130" },
{ extra = "rocm" },
Expand All @@ -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"
Expand All @@ -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" },
Expand All @@ -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`)
Expand Down Expand Up @@ -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 = [
Expand Down Expand Up @@ -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"]

# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
Expand Down
5 changes: 3 additions & 2 deletions src/anomalib/data/dataclasses/torch/base.py
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Expand Up @@ -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.
Expand All @@ -43,6 +41,9 @@ class InferenceBatch(NamedTuple):
pred_mask: torch.Tensor | None = None


NumpyT = TypeVar("NumpyT")
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Copilot AI Nov 23, 2025

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[nitpick] Moving NumpyT after InferenceBatch appears to be for ordering purposes, but it creates a gap between related type definitions. Consider adding a comment explaining why this TypeVar is defined here rather than at the top with other type-related imports, especially since it's used by the ToNumpyMixin class below.

Copilot uses AI. Check for mistakes.


@dataclass
class ToNumpyMixin(Generic[NumpyT]):
"""Mixin for converting torch-based dataclasses to numpy.
Expand Down
16 changes: 9 additions & 7 deletions src/anomalib/data/datasets/image/datumaro.py
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Expand Up @@ -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"],
Expand Down
21 changes: 19 additions & 2 deletions src/anomalib/engine/engine.py
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Expand Up @@ -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.

Expand All @@ -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.
Expand Down Expand Up @@ -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,
Expand All @@ -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.

Expand All @@ -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.
Expand Down Expand Up @@ -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.

Expand All @@ -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.
Expand Down Expand Up @@ -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(
Expand All @@ -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.

Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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.

Expand Down Expand Up @@ -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.
Expand Down
2 changes: 1 addition & 1 deletion src/anomalib/metrics/threshold/f1_adaptive_threshold.py
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Expand Up @@ -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
Expand Down
10 changes: 6 additions & 4 deletions src/anomalib/models/components/base/anomalib_module.py
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Expand Up @@ -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:
Expand Down
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