🧹 Enable more pre-commit rules

model_api/python/model_api/adapters/onnx_adapter.py

@@ -164,8 +164,7 @@ def embed_preprocessing(
             resize_fn = partial(RESIZE_TYPES[resize_mode], size=target_shape)
         preproc_funcs.append(resize_fn)
         input_transform = InputTransform(brg2rgb, mean, scale)
-        preproc_funcs.append(input_transform.__call__)
-        preproc_funcs.append(partial(change_layout, layout=layout))
+        preproc_funcs.extend((input_transform.__call__, partial(change_layout, layout=layout)))
 
         self.preprocessor = reduce(
             lambda f, g: lambda x: f(g(x)),

model_api/python/model_api/adapters/ovms_adapter.py

@@ -77,7 +77,7 @@ def infer_async(self, dict_data, callback_data):
         # For models with single output ovmsclient returns ndarray with results,
         # so the dict must be created to correctly implement interface.
         if isinstance(raw_result, np.ndarray):
-            output_name = list(self.metadata["outputs"].keys())[0]
+            output_name = next(iter(self.metadata["outputs"].keys()))
             raw_result = {output_name: raw_result}
         self.callback_fn(raw_result, (lambda x: x, callback_data))
 

model_api/python/model_api/models/anomaly.py

@@ -87,7 +87,7 @@ def postprocess(self, outputs: dict[str, np.ndarray], meta: dict[str, Any]) -> A
         pred_label: str | None = None
         pred_mask: np.ndarray | None = None
         pred_boxes: np.ndarray | None = None
-        predictions = outputs[list(self.outputs)[0]]
+        predictions = outputs[next(iter(self.outputs))]
 
         if len(predictions.shape) == 1:
             pred_score = predictions

model_api/python/model_api/models/classification.py

@@ -7,6 +7,7 @@
 
 import copy
 import json
+from itertools import starmap
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -303,7 +304,7 @@ def get_multilabel_predictions(self, logits: np.ndarray) -> list[Label]:
                 scores.append(logits[i])
         labels = [self.labels[i] if self.labels else "" for i in indices]
 
-        return [Label(*data) for data in zip(indices, labels, scores)]
+        return list(starmap(Label, zip(indices, labels, scores)))
 
     def get_multiclass_predictions(self, outputs: dict) -> list[Label]:
         if self.embedded_topk:
@@ -314,7 +315,7 @@ def get_multiclass_predictions(self, outputs: dict) -> list[Label]:
             scoresTensor = softmax(outputs[self.out_layer_names[0]][0])
             indicesTensor = [int(np.argmax(scoresTensor))]
             labels = [self.labels[i] if self.labels else "" for i in indicesTensor]
-        return [Label(*data) for data in zip(indicesTensor, labels, scoresTensor)]
+        return list(starmap(Label, zip(indicesTensor, labels, scoresTensor)))
 
 
 def addOrFindSoftmaxAndTopkOutputs(inference_adapter: InferenceAdapter, topk: int, output_raw_scores: bool) -> None:

model_api/python/model_api/models/instance_segmentation.py

@@ -221,7 +221,7 @@ def postprocess(self, outputs: dict, meta: dict) -> InstanceSegmentationResult:
             labels=labels,
             scores=scores,
             masks=_masks,
-            label_names=label_names if label_names else None,
+            label_names=label_names or None,
             saliency_map=_average_and_normalize(saliency_maps),
             feature_vector=outputs.get(_feature_vector_name, np.ndarray(0)),
         )

model_api/python/model_api/models/sam_models.py

@@ -32,7 +32,7 @@ def __init__(
         preload: bool = False,
     ):
         super().__init__(inference_adapter, configuration, preload)
-        self.output_name: str = list(self.outputs.keys())[0]
+        self.output_name: str = next(iter(self.outputs.keys()))
         self.resize_type: str
         self.image_size: int
 

model_api/python/model_api/models/visual_prompting.py

@@ -484,9 +484,9 @@ def _predict_masks(
             elif i == 1:
                 # Cascaded Post-refinement-1
                 mask_input, masks, _ = _decide_masks(
-                    masks,
-                    logits,
-                    scores,
+                    masks,  # noqa: F821 masks are set in the first iteration
+                    logits,  # noqa: F821 masks are set in the first iteration
+                    scores,  # noqa: F821 masks are set in the first iteration
                     is_single=True,
                 )
                 if masks.sum() == 0:
@@ -498,8 +498,8 @@ def _predict_masks(
                 # Cascaded Post-refinement-2
                 mask_input, masks, _ = _decide_masks(
                     masks,
-                    logits,
-                    scores,
+                    logits,  # noqa: F821 masks are set in the first iteration
+                    scores,  # noqa: F821 masks are set in the first iteration
                 )
                 if masks.sum() == 0:
                     return {"upscaled_masks": masks}

model_api/python/model_api/tilers/instance_segmentation.py

@@ -132,7 +132,7 @@ def _merge_results(self, results, shape) -> InstanceSegmentationResult:
             labels=labels.squeeze(),
             scores=scores.squeeze(),
             masks=resized_masks,
-            label_names=label_names if label_names else None,
+            label_names=label_names or None,
             saliency_map=saliency_map,
             feature_vector=merged_vector,
         )

model_api/python/model_api/tilers/tiler.py

@@ -12,7 +12,7 @@
 
 
 class Tiler(abc.ABC):
-    EXECUTION_MODES = ["async", "sync"]
+    EXECUTION_MODES = ("async", "sync")
     """
     An abstract tiler
 

model_api/python/pyproject.toml

@@ -91,7 +91,7 @@ preview = true
 
 # Enable rules
 lint.select = [
-    # "F",    # Pyflakes (`F`)
+    "F",    # Pyflakes (`F`)
     "E",    # pycodestyle error (`E`)
     "W",    # pycodestyle warning (`W`)
     "C90",  # mccabe (`C90`)
@@ -127,18 +127,18 @@ lint.select = [
     # "ARG",  # flake8-unsused-arguments (`ARG`)
     "PTH",  # flake8-use-pathlib (`PTH`)
     # "TD",   # flake8-todos (`TD`)
-    # "FIX",  # flake8-fixme (`FIX`)
+    "FIX",  # flake8-fixme (`FIX`)
     "ERA",  # eradicate (`ERA`)
     "PD",   # pandas-vet (`PD`)
     "PGH",  # pygrep-hooks (`PGH`)
     # "PL",   # pylint (`PL`)
     # "TRY",  # tryceratos (`TRY`)
-    # "FLY",  # flynt (`FLY`)
+    "FLY",  # flynt (`FLY`)
     "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`
+    "RUF",  # Ruff-specific rules (`RUF`)
+    "FURB", # refurb (`FURB`) - ERROR: Unknown rule selector: `FURB`
+    "LOG",  # flake8-logging (`LOG`) - ERROR: Unknown rule selector: `LOG`
 ]
 
 lint.ignore = [