Add missing None type hint to init functions

Author: ProGamerGov

torchvision/datasets/_optical_flow.py

@@ -28,7 +28,7 @@ class FlowDataset(ABC, VisionDataset):
     # and it's up to whatever consumes the dataset to decide what valid_flow_mask should be.
     _has_builtin_flow_mask = False
 
-    def __init__(self, root, transforms=None):
+    def __init__(self, root, transforms=None) -> None:
 
         super().__init__(root=root)
         self.transforms = transforms
@@ -118,7 +118,7 @@ class Sintel(FlowDataset):
             return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
     """
 
-    def __init__(self, root, split="train", pass_name="clean", transforms=None):
+    def __init__(self, root, split="train", pass_name="clean", transforms=None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))
@@ -180,7 +180,7 @@ class KittiFlow(FlowDataset):
 
     _has_builtin_flow_mask = True
 
-    def __init__(self, root, split="train", transforms=None):
+    def __init__(self, root, split="train", transforms=None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))
@@ -245,7 +245,7 @@ class FlyingChairs(FlowDataset):
             return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
     """
 
-    def __init__(self, root, split="train", transforms=None):
+    def __init__(self, root, split="train", transforms=None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "val"))
@@ -316,7 +316,7 @@ class FlyingThings3D(FlowDataset):
             return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
     """
 
-    def __init__(self, root, split="train", pass_name="clean", camera="left", transforms=None):
+    def __init__(self, root, split="train", pass_name="clean", camera="left", transforms=None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))
@@ -401,7 +401,7 @@ class HD1K(FlowDataset):
 
     _has_builtin_flow_mask = True
 
-    def __init__(self, root, split="train", transforms=None):
+    def __init__(self, root, split="train", transforms=None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))

torchvision/datasets/_stereo_matching.py

@@ -20,7 +20,7 @@ class StereoMatchingDataset(ABC, VisionDataset):
 
     _has_built_in_disparity_mask = False
 
-    def __init__(self, root: str, transforms: Optional[Callable] = None):
+    def __init__(self, root: str, transforms: Optional[Callable] = None) -> None:
         """
         Args:
             root(str): Root directory of the dataset.
@@ -152,7 +152,7 @@ class CarlaStereo(StereoMatchingDataset):
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
-    def __init__(self, root: str, transforms: Optional[Callable] = None):
+    def __init__(self, root: str, transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         root = Path(root) / "carla-highres"
@@ -229,7 +229,7 @@ class Kitti2012Stereo(StereoMatchingDataset):
 
     _has_built_in_disparity_mask = True
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None):
+    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))
@@ -317,7 +317,7 @@ class Kitti2015Stereo(StereoMatchingDataset):
 
     _has_built_in_disparity_mask = True
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None):
+    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))

torchvision/datasets/folder.py

@@ -305,7 +305,7 @@ def __init__(
         target_transform: Optional[Callable] = None,
         loader: Callable[[str], Any] = default_loader,
         is_valid_file: Optional[Callable[[str], bool]] = None,
-    ):
+    ) -> None:
         super().__init__(
             root,
             loader,

torchvision/datasets/kitti.py

@@ -57,7 +57,7 @@ def __init__(
         target_transform: Optional[Callable] = None,
         transforms: Optional[Callable] = None,
         download: bool = False,
-    ):
+    ) -> None:
         super().__init__(
             root,
             transform=transform,

torchvision/datasets/lfw.py

@@ -38,7 +38,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
-    ):
+    ) -> None:
         super().__init__(os.path.join(root, self.base_folder), transform=transform, target_transform=target_transform)
 
         self.image_set = verify_str_arg(image_set.lower(), "image_set", self.file_dict.keys())
@@ -119,7 +119,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
-    ):
+    ) -> None:
         super().__init__(root, split, image_set, "people", transform, target_transform, download)
 
         self.class_to_idx = self._get_classes()
@@ -201,7 +201,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
-    ):
+    ) -> None:
         super().__init__(root, split, image_set, "pairs", transform, target_transform, download)
 
         self.pair_names, self.data, self.targets = self._get_pairs(self.images_dir)

torchvision/datasets/oxford_iiit_pet.py

@@ -45,7 +45,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
-    ):
+    ) -> None:
         self._split = verify_str_arg(split, "split", ("trainval", "test"))
         if isinstance(target_types, str):
             target_types = [target_types]

torchvision/datasets/pcam.py

@@ -73,7 +73,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
-    ):
+    ) -> None:
         try:
             import h5py
 

torchvision/datasets/voc.py

@@ -75,7 +75,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         transforms: Optional[Callable] = None,
-    ):
+    ) -> None:
         super().__init__(root, transforms, transform, target_transform)
         if year == "2007-test":
             if image_set == "test":

torchvision/models/_api.py

@@ -48,7 +48,7 @@ class WeightsEnum(StrEnum):
         value (Weights): The data class entry with the weight information.
     """
 
-    def __init__(self, value: Weights):
+    def __init__(self, value: Weights) -> None:
         self._value_ = value
 
     @classmethod

torchvision/models/detection/anchor_utils.py

@@ -159,7 +159,7 @@ def __init__(
         scales: Optional[List[float]] = None,
         steps: Optional[List[int]] = None,
         clip: bool = True,
-    ):
+    ) -> None:
         super().__init__()
         if steps is not None and len(aspect_ratios) != len(steps):
             raise ValueError("aspect_ratios and steps should have the same length")