Bump python to 3.14 and ruff to latest

Author: tybulewicz

.github/workflows/pre_commit.yml

@@ -48,10 +48,10 @@ jobs:
           - "ubuntu-latest"
           - "windows-latest"
         python-version:
-          - "3.10"
           - "3.11"
           - "3.12"
           - "3.13"
+          - "3.14"
     runs-on: ${{ matrix.os }}
     steps:
       - *checkout
@@ -82,10 +82,10 @@ jobs:
           - "ubuntu-24.04"
           - "windows-2022"
         python-version:
-          - "3.10"
           - "3.11"
           - "3.12"
           - "3.13"
+          - "3.14"
     name: unit & functional tests (${{ matrix.os }}, Python ${{ matrix.python-version }})
     runs-on: ${{ matrix.os }}
     steps:

.pre-commit-config.yaml

@@ -14,8 +14,8 @@ repos:
       - id: detect-private-key
 
   # Ruff version.
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: "v0.6.2"
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: "v0.15.2"
     hooks:
       # Run the linter.
       - id: ruff

.python-version

@@ -1 +1 @@
-3.10
+3.14

pyproject.toml

@@ -10,7 +10,7 @@ build-backend = "hatchling.build"
 [project]
 name = "openvino_model_api"
 version = "0.4.0.0"
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 authors = [
   {name = "Intel(R) Corporation"},
 ]
@@ -20,13 +20,17 @@ maintainers = [
 description = "Model API: model wrappers and pipelines for inference with OpenVINO"
 readme = "README.md"
 classifiers = [
-  "License :: OSI Approved :: Apache Software License",
-  "Programming Language :: Python :: 3.10"
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
 ]
 dependencies = [
     "numpy>=1.16.6",
+    "onnxruntime",
     "opencv-python-headless",
-    "openvino>=2025.3",
+    "openvino==2025.4.1",
     "pillow",
 ]
 
@@ -220,8 +224,8 @@ line-length = 120
 # Allow unused variables when underscore-prefixed.
 lint.dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
 
-# Assume Python 3.10.
-target-version = "py310"
+# Assume Python 3.14.
+target-version = "py314"
 
 # Allow imports relative to the "src" and "tests" directories.
 src = ["src", "tests"]

src/model_api/models/classification.py

@@ -7,7 +7,6 @@
 
 import copy
 import json
-from itertools import starmap
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -251,21 +250,21 @@ def get_multilabel_predictions(self, logits: np.ndarray) -> list[Label]:
         labels_list = self.params.labels
         labels = [labels_list[i] if labels_list else "" for i in indices]
 
-        return list(starmap(Label, zip(indices, labels, scores)))
+        return list(map(Label, indices, labels, scores))
 
     def get_multiclass_predictions(self, outputs: dict) -> list[Label]:
         axis = 1
         logits = outputs[self.out_layer_names[0]]
         if not is_softmaxed(logits, axis=axis):
             logits = softmax(logits, axis=axis)
         top_k_result = top_k(logits, self.params.topk, axis=axis)
-        scores = top_k_result.values[0]  # noqa: PD011 # silencing false positive - it's not pandas code
+        scores = top_k_result.values[0]
         indices = top_k_result.indices[0]
 
         labels_list = self.params.labels
         labels = [labels_list[i] if labels_list else "" for i in indices]
 
-        return list(starmap(Label, zip(indices, labels, scores)))
+        return list(map(Label, indices, labels, scores))
 
 
 def sigmoid_numpy(x: np.ndarray) -> np.ndarray:

src/model_api/models/parameters.py

@@ -45,7 +45,7 @@ def __dir__(self) -> list[str]:
         try:
             parameters = self._model.get_cached_parameters()
             return list(parameters.keys())
-        except (AttributeError, TypeError, ValueError):
+        except AttributeError, TypeError, ValueError:
             return []
 
 

src/model_api/models/yolo.py

@@ -761,8 +761,7 @@ def parameters(cls):
             {
                 "agnostic_nms": BooleanValue(
                     description=(
-                        "If True, the model is agnostic to the number of classes, "
-                        "and all classes are considered as one"
+                        "If True, the model is agnostic to the number of classes, and all classes are considered as one"
                     ),
                     default_value=False,
                 ),

tests/accuracy/download_models.py

@@ -37,8 +37,7 @@ async def stream_file(client, url, filename, semaphore):
 
 async def download_single_image(client, url, filename):
     image = await client.get(url)
-    with Path(filename).open("wb") as im:
-        im.write(image.content)
+    Path(filename).write_bytes(image.content)
 
 
 async def download_images(data_dir):

tests/accuracy/test_accuracy.py

@@ -223,9 +223,9 @@ def compare_detection_result(outputs: DetectionResult, reference: dict) -> None:
     if expected_bboxes.size == 0 and outputs.bboxes.size == 0:
         expected_bboxes = expected_bboxes.reshape(0, 4)
 
-    assert (
-        outputs.bboxes.shape == expected_bboxes.shape
-    ), f"bboxes shape mismatch: {outputs.bboxes.shape} vs {expected_bboxes.shape}"
+    assert outputs.bboxes.shape == expected_bboxes.shape, (
+        f"bboxes shape mismatch: {outputs.bboxes.shape} vs {expected_bboxes.shape}"
+    )
 
     # Sort both outputs and expected by bbox coordinates (x1, y1, x2, y2) for deterministic comparison
     output_sort_indices = np.lexsort((

tools/model_converter/model_converter.py

@@ -189,8 +189,7 @@ def create_model(
                 elif "state_dict" in checkpoint:
                     # Cannot reconstruct architecture from state_dict alone
                     error_msg = (
-                        "Checkpoint contains only state_dict. "
-                        "Please specify the model class instead of torch.nn.Module"
+                        "Checkpoint contains only state_dict. Please specify the model class instead of torch.nn.Module"
                     )
                     raise ValueError(error_msg)
                 else: