Fix linter

Author: datumbox

torchvision/prototype/datasets/_builtin/caltech.py

@@ -110,7 +110,9 @@ def _prepare_sample(
             image=image,
             ann_path=ann_path,
             bounding_box=BoundingBox(
-                ann["box_coord"].astype(np.int64).squeeze()[[2, 0, 3, 1]], format="xyxy", spatial_size=image.spatial_size
+                ann["box_coord"].astype(np.int64).squeeze()[[2, 0, 3, 1]],
+                format="xyxy",
+                spatial_size=image.spatial_size,
             ),
             contour=_Feature(ann["obj_contour"].T),
         )

torchvision/prototype/datasets/_builtin/coco.py

@@ -97,7 +97,9 @@ def _resources(self) -> List[OnlineResource]:
         )
         return [images, meta]
 
-    def _segmentation_to_mask(self, segmentation: Any, *, is_crowd: bool, spatial_size: Tuple[int, int]) -> torch.Tensor:
+    def _segmentation_to_mask(
+        self, segmentation: Any, *, is_crowd: bool, spatial_size: Tuple[int, int]
+    ) -> torch.Tensor:
         from pycocotools import mask
 
         if is_crowd:
@@ -115,7 +117,9 @@ def _decode_instances_anns(self, anns: List[Dict[str, Any]], image_meta: Dict[st
             segmentations=_Feature(
                 torch.stack(
                     [
-                        self._segmentation_to_mask(ann["segmentation"], is_crowd=ann["iscrowd"], spatial_size=spatial_size)
+                        self._segmentation_to_mask(
+                            ann["segmentation"], is_crowd=ann["iscrowd"], spatial_size=spatial_size
+                        )
                         for ann in anns
                     ]
                 )

torchvision/prototype/datasets/_builtin/cub200.py

@@ -149,7 +149,9 @@ def _2010_anns_key(self, data: Tuple[str, BinaryIO]) -> Tuple[str, Tuple[str, Bi
         path = pathlib.Path(data[0])
         return path.with_suffix(".jpg").name, data
 
-    def _2010_prepare_ann(self, data: Tuple[str, Tuple[str, BinaryIO]], spatial_size: Tuple[int, int]) -> Dict[str, Any]:
+    def _2010_prepare_ann(
+        self, data: Tuple[str, Tuple[str, BinaryIO]], spatial_size: Tuple[int, int]
+    ) -> Dict[str, Any]:
         _, (path, buffer) = data
         content = read_mat(buffer)
         return dict(

torchvision/prototype/features/_bounding_box.py

@@ -84,7 +84,9 @@ def resize(  # type: ignore[override]
         max_size: Optional[int] = None,
         antialias: bool = False,
     ) -> BoundingBox:
-        output, spatial_size = self._F.resize_bounding_box(self, spatial_size=self.spatial_size, size=size, max_size=max_size)
+        output, spatial_size = self._F.resize_bounding_box(
+            self, spatial_size=self.spatial_size, size=size, max_size=max_size
+        )
         return BoundingBox.wrap_like(self, output, spatial_size=spatial_size)
 
     def crop(self, top: int, left: int, height: int, width: int) -> BoundingBox: