Update code for new versions of linters

datumaro/components/dataset_generator.py

@@ -18,5 +18,4 @@ def generate(cls, output_dir, count, shape, **options):
         return generator.generate_dataset()
 
     @abstractmethod
-    def generate_dataset(self):
-        ...
+    def generate_dataset(self): ...

datumaro/components/format_detection.py

@@ -445,8 +445,7 @@ def __call__(
         format_name: str,
         reason: RejectionReason,
         human_message: str,
-    ) -> Any:
-        ...
+    ) -> Any: ...
 
 
 def detect_dataset_format(

datumaro/plugins/cityscapes_format.py

@@ -442,9 +442,11 @@ def apply(self):
                 compiled_mask = CompiledMask.from_instance_masks(
                     masks,
                     instance_ids=[
-                        self._label_id_mapping(m.label)
-                        if m.attributes.get("is_crowd", False)
-                        else self._label_id_mapping(m.label) * 1000 + (m.id or (i + 1))
+                        (
+                            self._label_id_mapping(m.label)
+                            if m.attributes.get("is_crowd", False)
+                            else self._label_id_mapping(m.label) * 1000 + (m.id or (i + 1))
+                        )
                         for i, m in enumerate(masks)
                     ],
                     instance_labels=[self._label_id_mapping(m.label) for m in masks],

datumaro/plugins/coco_format/converter.py

@@ -566,9 +566,8 @@ def _split_tasks_string(s):
 
     @classmethod
     def build_cmdline_parser(cls, **kwargs):
-        kwargs[
-            "description"
-        ] = """
+        kwargs["description"] = (
+            """
             Segmentation mask modes ('--segmentation-mode'):|n
             - '{sm.guess.name}': guess the mode for each instance,|n
             |s|suse 'is_crowd' attribute as hint|n
@@ -592,7 +591,8 @@ def build_cmdline_parser(cls, **kwargs):
             directory, otherwise they are saved in separate directories
             by subsets.
             """.format(
-            sm=SegmentationMode
+                sm=SegmentationMode
+            )
         )
         parser = super().build_cmdline_parser(**kwargs)
         parser.add_argument(

datumaro/plugins/coco_format/extractor.py

@@ -292,12 +292,10 @@ def _get_label_id(self, ann):
         return label_id
 
     @overload
-    def _parse_field(self, ann: Dict[str, Any], key: str, cls: Type[T]) -> T:
-        ...
+    def _parse_field(self, ann: Dict[str, Any], key: str, cls: Type[T]) -> T: ...
 
     @overload
-    def _parse_field(self, ann: Dict[str, Any], key: str, cls: Tuple[Type, ...]) -> Any:
-        ...
+    def _parse_field(self, ann: Dict[str, Any], key: str, cls: Tuple[Type, ...]) -> Any: ...
 
     def _parse_field(
         self, ann: Dict[str, Any], key: str, cls: Union[Type[T], Tuple[Type, ...]]

datumaro/plugins/sampler/random_sampler.py

@@ -67,7 +67,7 @@ def __init__(
 
     def __iter__(self):
         if self._indices is None:
-            rng = Random(self._seed)
+            rng = Random(self._seed)  # nosec - disable B311
 
             if self._subset:
                 n = len(self._extractor.get_subset(self._subset))
@@ -213,7 +213,7 @@ def _make_bucket():
 
         buckets = defaultdict(_make_bucket)  # subset -> subset_buckets
 
-        rng = Random(self._seed)
+        rng = Random(self._seed)  # nosec - disable B311
 
         for i, item in enumerate(self._extractor):
             labels = set(getattr(ann, "label", None) for ann in item.annotations)

datumaro/plugins/synthetic_data/image_generator.py

@@ -66,7 +66,10 @@ def generate_dataset(self) -> None:
 
         mp_ctx = get_context("spawn")  # On Mac 10.15 and Python 3.7 fork leads to hangs
         with mp_ctx.Pool(processes=self._cpu_count) as pool:
-            params = pool.map(self._generate_category, [Random(i) for i in range(self._categories)])
+            params = pool.map(
+                self._generate_category,
+                [Random(i) for i in range(self._categories)],  # nosec - disable B311
+            )
 
         instances_weights = np.repeat(self._weights, self._instances, axis=0)
         weight_per_img = np.tile(instances_weights, (self._categories, 1))
@@ -110,7 +113,7 @@ def _generate_image_batch(
 
         for i, param, w in zip(indices, params, weights):
             image = self._generate_image(
-                Random(i),
+                Random(i),  # nosec - disable B311
                 param,
                 self._iterations,
                 self._height,
@@ -119,7 +122,7 @@ def _generate_image_batch(
                 weight=w,
             )
             color_image = colorize(image, net)
-            aug_image = augment(Random(i), color_image, background_colors)
+            aug_image = augment(Random(i), color_image, background_colors)  # nosec - disable B311
             save_image(
                 osp.join(self._output_dir, "{:06d}.png".format(i)), aug_image, create_dir=True
             )

datumaro/plugins/tf_detection_api_format/converter.py

@@ -73,8 +73,8 @@ def apply(self):
         os.makedirs(self._save_dir, exist_ok=True)
 
         label_categories = self._extractor.categories().get(AnnotationType.label, LabelCategories())
-        get_label = (
-            lambda label_id: label_categories.items[label_id].name if label_id is not None else ""
+        get_label = lambda label_id: (
+            label_categories.items[label_id].name if label_id is not None else ""
         )
         label_ids = OrderedDict(
             (label.name, 1 + idx) for idx, label in enumerate(label_categories.items)