From 3586b535e69559a4a75113daf650bc1fb2bc99a0 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 11:45:59 +0100
Subject: [PATCH 1/9] Rewrite `get_dimensions`, `get_num_channels` and
 `get_spatial_size`

---
 .../transforms/functional/__init__.py         |  6 +++
 .../prototype/transforms/functional/_meta.py  | 41 ++++++++++++++++---
 2 files changed, 42 insertions(+), 5 deletions(-)

diff --git a/torchvision/prototype/transforms/functional/__init__.py b/torchvision/prototype/transforms/functional/__init__.py
index f081d101dff..1e5753f2cfb 100644
--- a/torchvision/prototype/transforms/functional/__init__.py
+++ b/torchvision/prototype/transforms/functional/__init__.py
@@ -7,9 +7,15 @@
     convert_color_space_image_tensor,
     convert_color_space_image_pil,
     convert_color_space,
+    get_dimensions_image_tensor,
+    get_dimensions_image_pil,
     get_dimensions,
     get_image_num_channels,
+    get_num_channels_image_tensor,
+    get_num_channels_image_pil,
     get_num_channels,
+    get_spatial_size_image_tensor,
+    get_spatial_size_image_pil,
     get_spatial_size,
 )  # usort: skip
 
diff --git a/torchvision/prototype/transforms/functional/_meta.py b/torchvision/prototype/transforms/functional/_meta.py
index 90cfffcf276..d3c62325102 100644
--- a/torchvision/prototype/transforms/functional/_meta.py
+++ b/torchvision/prototype/transforms/functional/_meta.py
@@ -30,12 +30,28 @@ def get_chw(image: features.ImageTypeJIT) -> Tuple[int, int, int]:
 
 
 def get_dimensions(image: features.ImageTypeJIT) -> List[int]:
-    return list(get_chw(image))
+    if isinstance(image, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(image, features.Image)):
+        return get_dimensions_image_tensor(image)
+    elif isinstance(image, features.Image):
+        channels = image.num_channels
+        height, width = image.image_size
+        return [channels, height, width]
+    else:
+        return get_dimensions_image_pil(image)
+
+
+get_num_channels_image_tensor = _FT.get_image_num_channels
+get_num_channels_image_pil = _FP.get_image_num_channels
 
 
 def get_num_channels(image: features.ImageTypeJIT) -> int:
-    num_channels, *_ = get_chw(image)
-    return num_channels
+    if isinstance(image, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(image, features.Image)):
+        channels = _FT.get_image_num_channels(image)
+    elif isinstance(image, features.Image):
+        channels = image.num_channels
+    else:
+        channels = _FP.get_image_num_channels(image)
+    return channels
 
 
 # We changed the names to ensure it can be used not only for images but also videos. Thus, we just alias it without
@@ -43,9 +59,24 @@ def get_num_channels(image: features.ImageTypeJIT) -> int:
 get_image_num_channels = get_num_channels
 
 
+def get_spatial_size_image_tensor(image: torch.Tensor) -> List[int]:
+    width, height = _FT.get_image_size(image)
+    return [height, width]
+
+
+@torch.jit.unused
+def get_spatial_size_image_pil(image: PIL.Image.Image) -> List[int]:
+    width, height = _FP.get_image_size(image)
+    return [height, width]
+
+
 def get_spatial_size(image: features.ImageTypeJIT) -> List[int]:
-    _, *size = get_chw(image)
-    return size
+    if isinstance(image, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(image, features.Image)):
+        return get_spatial_size_image_tensor(image)
+    elif isinstance(image, features.Image):
+        return list(image.image_size)
+    else:
+        return get_spatial_size_image_pil(image)
 
 
 def _xywh_to_xyxy(xywh: torch.Tensor) -> torch.Tensor:

From d527fbbc9f94d9072c9f51efed615dae96ee9056 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 11:48:22 +0100
Subject: [PATCH 2/9] Remove `get_chw`

---
 torchvision/prototype/transforms/_auto_augment.py    | 10 +++++-----
 torchvision/prototype/transforms/_utils.py           |  4 ++--
 torchvision/prototype/transforms/functional/_meta.py | 12 ------------
 3 files changed, 7 insertions(+), 19 deletions(-)

diff --git a/torchvision/prototype/transforms/_auto_augment.py b/torchvision/prototype/transforms/_auto_augment.py
index c98e5c36e4a..9f348cd4972 100644
--- a/torchvision/prototype/transforms/_auto_augment.py
+++ b/torchvision/prototype/transforms/_auto_augment.py
@@ -7,7 +7,7 @@
 from torch.utils._pytree import tree_flatten, tree_unflatten
 from torchvision.prototype import features
 from torchvision.prototype.transforms import AutoAugmentPolicy, functional as F, InterpolationMode, Transform
-from torchvision.prototype.transforms.functional._meta import get_chw
+from torchvision.prototype.transforms.functional._meta import get_spatial_size
 
 from ._utils import _isinstance, _setup_fill_arg
 
@@ -277,7 +277,7 @@ def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
         id, image = self._extract_image(sample)
-        _, height, width = get_chw(image)
+        height, width = get_spatial_size(image)
 
         policy = self._policies[int(torch.randint(len(self._policies), ()))]
 
@@ -348,7 +348,7 @@ def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
         id, image = self._extract_image(sample)
-        _, height, width = get_chw(image)
+        height, width = get_spatial_size(image)
 
         for _ in range(self.num_ops):
             transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
@@ -402,7 +402,7 @@ def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
         id, image = self._extract_image(sample)
-        _, height, width = get_chw(image)
+        height, width = get_spatial_size(image)
 
         transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
 
@@ -472,7 +472,7 @@ def _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor:
     def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
         id, orig_image = self._extract_image(sample)
-        _, height, width = get_chw(orig_image)
+        height, width = get_spatial_size(orig_image)
 
         if isinstance(orig_image, torch.Tensor):
             image = orig_image
diff --git a/torchvision/prototype/transforms/_utils.py b/torchvision/prototype/transforms/_utils.py
index 219e6e50586..7db4f710481 100644
--- a/torchvision/prototype/transforms/_utils.py
+++ b/torchvision/prototype/transforms/_utils.py
@@ -10,7 +10,7 @@
 from torchvision.prototype import features
 from torchvision.prototype.features._feature import FillType
 
-from torchvision.prototype.transforms.functional._meta import get_chw
+from torchvision.prototype.transforms.functional._meta import get_dimensions
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
 
 from typing_extensions import Literal
@@ -80,7 +80,7 @@ def query_bounding_box(sample: Any) -> features.BoundingBox:
 def query_chw(sample: Any) -> Tuple[int, int, int]:
     flat_sample, _ = tree_flatten(sample)
     chws = {
-        get_chw(item)
+        get_dimensions(item)
         for item in flat_sample
         if isinstance(item, (features.Image, PIL.Image.Image)) or features.is_simple_tensor(item)
     }
diff --git a/torchvision/prototype/transforms/functional/_meta.py b/torchvision/prototype/transforms/functional/_meta.py
index d3c62325102..725ed802382 100644
--- a/torchvision/prototype/transforms/functional/_meta.py
+++ b/torchvision/prototype/transforms/functional/_meta.py
@@ -10,18 +10,6 @@
 get_dimensions_image_pil = _FP.get_dimensions
 
 
-# TODO: Should this be prefixed with `_` similar to other methods that don't get exposed by init?
-def get_chw(image: features.ImageTypeJIT) -> Tuple[int, int, int]:
-    if isinstance(image, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(image, features.Image)):
-        channels, height, width = get_dimensions_image_tensor(image)
-    elif isinstance(image, features.Image):
-        channels = image.num_channels
-        height, width = image.image_size
-    else:  # isinstance(image, PIL.Image.Image)
-        channels, height, width = get_dimensions_image_pil(image)
-    return channels, height, width
-
-
 # The three functions below are here for BC. Whether we want to have two different kernels and how they and the
 # compound version should be named is still under discussion: https://github.com/pytorch/vision/issues/6491
 # Given that these kernels should also support boxes, masks, and videos, it is unlikely that there name will stay.

From c9ba0fd60b7ab464955ec35396347ca26a59dfe5 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 11:49:24 +0100
Subject: [PATCH 3/9] Remove comments

---
 torchvision/prototype/transforms/functional/_meta.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/torchvision/prototype/transforms/functional/_meta.py b/torchvision/prototype/transforms/functional/_meta.py
index 725ed802382..fbe05473857 100644
--- a/torchvision/prototype/transforms/functional/_meta.py
+++ b/torchvision/prototype/transforms/functional/_meta.py
@@ -6,17 +6,11 @@
 from torchvision.prototype.features import BoundingBoxFormat, ColorSpace
 from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
 
+
 get_dimensions_image_tensor = _FT.get_dimensions
 get_dimensions_image_pil = _FP.get_dimensions
 
 
-# The three functions below are here for BC. Whether we want to have two different kernels and how they and the
-# compound version should be named is still under discussion: https://github.com/pytorch/vision/issues/6491
-# Given that these kernels should also support boxes, masks, and videos, it is unlikely that there name will stay.
-# They will either be deprecated or simply aliased to the new kernels if we have reached consensus about the issue
-# detailed above.
-
-
 def get_dimensions(image: features.ImageTypeJIT) -> List[int]:
     if isinstance(image, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(image, features.Image)):
         return get_dimensions_image_tensor(image)

From a059af2f351271e49884656f5bfb4f223e84946c Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 12:18:40 +0100
Subject: [PATCH 4/9] Make `get_spatial_size` support non-image input

---
 torchvision/prototype/features/_mask.py        |  7 ++++++-
 .../prototype/transforms/functional/_meta.py   | 18 +++++++++++-------
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/torchvision/prototype/features/_mask.py b/torchvision/prototype/features/_mask.py
index 9dd614752a6..e5acd4b05fb 100644
--- a/torchvision/prototype/features/_mask.py
+++ b/torchvision/prototype/features/_mask.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import List, Optional, Union
+from typing import cast, List, Optional, Tuple, Union
 
 import torch
 from torchvision.transforms import InterpolationMode
@@ -9,6 +9,11 @@
 
 
 class Mask(_Feature):
+
+    @property
+    def image_size(self) -> Tuple[int, int]:
+        return cast(Tuple[int, int], tuple(self.shape[-2:]))
+
     def horizontal_flip(self) -> Mask:
         output = self._F.horizontal_flip_mask(self)
         return Mask.new_like(self, output)
diff --git a/torchvision/prototype/transforms/functional/_meta.py b/torchvision/prototype/transforms/functional/_meta.py
index fbe05473857..f3fbdd73b2b 100644
--- a/torchvision/prototype/transforms/functional/_meta.py
+++ b/torchvision/prototype/transforms/functional/_meta.py
@@ -1,4 +1,4 @@
-from typing import cast, List, Optional, Tuple
+from typing import cast, List, Optional, Tuple, Union
 
 import PIL.Image
 import torch
@@ -52,13 +52,17 @@ def get_spatial_size_image_pil(image: PIL.Image.Image) -> List[int]:
     return [height, width]
 
 
-def get_spatial_size(image: features.ImageTypeJIT) -> List[int]:
-    if isinstance(image, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(image, features.Image)):
-        return get_spatial_size_image_tensor(image)
-    elif isinstance(image, features.Image):
-        return list(image.image_size)
+def get_spatial_size(inpt: features.InputTypeJIT) -> List[int]:
+    if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features._Feature)):
+        return get_spatial_size_image_tensor(inpt)
+    elif isinstance(inpt, features._Feature):
+        image_size = getattr(inpt, "image_size", None)
+        if image_size is not None:
+            return list(image_size)
+        else:
+            raise ValueError(f"Type {inpt.__class__} doesn't have spatial size.")
     else:
-        return get_spatial_size_image_pil(image)
+        return get_spatial_size_image_pil(inpt)
 
 
 def _xywh_to_xyxy(xywh: torch.Tensor) -> torch.Tensor:

From a10ce7afb4c3618fc1b42e51c01760d5bff66248 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 14:22:53 +0100
Subject: [PATCH 5/9] Reduce the unnecessary use of `get_dimensions*`

---
 torchvision/prototype/features/_mask.py       |  1 -
 .../transforms/functional/_geometry.py        | 21 ++++++++++++-------
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/torchvision/prototype/features/_mask.py b/torchvision/prototype/features/_mask.py
index e5acd4b05fb..3d50c2d8101 100644
--- a/torchvision/prototype/features/_mask.py
+++ b/torchvision/prototype/features/_mask.py
@@ -9,7 +9,6 @@
 
 
 class Mask(_Feature):
-
     @property
     def image_size(self) -> Tuple[int, int]:
         return cast(Tuple[int, int], tuple(self.shape[-2:]))
diff --git a/torchvision/prototype/transforms/functional/_geometry.py b/torchvision/prototype/transforms/functional/_geometry.py
index 7a291967bfd..21cd2008c3a 100644
--- a/torchvision/prototype/transforms/functional/_geometry.py
+++ b/torchvision/prototype/transforms/functional/_geometry.py
@@ -21,7 +21,12 @@
     interpolate,
 )
 
-from ._meta import convert_format_bounding_box, get_dimensions_image_pil, get_dimensions_image_tensor
+from ._meta import (
+    convert_format_bounding_box,
+    get_dimensions_image_tensor,
+    get_spatial_size_image_pil,
+    get_spatial_size_image_tensor,
+)
 
 horizontal_flip_image_tensor = _FT.hflip
 horizontal_flip_image_pil = _FP.hflip
@@ -305,7 +310,7 @@ def affine_image_pil(
     # it is visually better to estimate the center without 0.5 offset
     # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine
     if center is None:
-        _, height, width = get_dimensions_image_pil(image)
+        height, width = get_spatial_size_image_pil(image)
         center = [width * 0.5, height * 0.5]
     matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
 
@@ -1071,13 +1076,13 @@ def _center_crop_compute_crop_anchor(
 
 def center_crop_image_tensor(image: torch.Tensor, output_size: List[int]) -> torch.Tensor:
     crop_height, crop_width = _center_crop_parse_output_size(output_size)
-    _, image_height, image_width = get_dimensions_image_tensor(image)
+    image_height, image_width = get_spatial_size_image_tensor(image)
 
     if crop_height > image_height or crop_width > image_width:
         padding_ltrb = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width)
         image = pad_image_tensor(image, padding_ltrb, fill=0)
 
-        _, image_height, image_width = get_dimensions_image_tensor(image)
+        image_height, image_width = get_spatial_size_image_tensor(image)
         if crop_width == image_width and crop_height == image_height:
             return image
 
@@ -1088,13 +1093,13 @@ def center_crop_image_tensor(image: torch.Tensor, output_size: List[int]) -> tor
 @torch.jit.unused
 def center_crop_image_pil(image: PIL.Image.Image, output_size: List[int]) -> PIL.Image.Image:
     crop_height, crop_width = _center_crop_parse_output_size(output_size)
-    _, image_height, image_width = get_dimensions_image_pil(image)
+    image_height, image_width = get_spatial_size_image_pil(image)
 
     if crop_height > image_height or crop_width > image_width:
         padding_ltrb = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width)
         image = pad_image_pil(image, padding_ltrb, fill=0)
 
-        _, image_height, image_width = get_dimensions_image_pil(image)
+        image_height, image_width = get_spatial_size_image_pil(image)
         if crop_width == image_width and crop_height == image_height:
             return image
 
@@ -1228,7 +1233,7 @@ def five_crop_image_tensor(
     image: torch.Tensor, size: List[int]
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     crop_height, crop_width = _parse_five_crop_size(size)
-    _, image_height, image_width = get_dimensions_image_tensor(image)
+    image_height, image_width = get_spatial_size_image_tensor(image)
 
     if crop_width > image_width or crop_height > image_height:
         msg = "Requested crop size {} is bigger than input size {}"
@@ -1248,7 +1253,7 @@ def five_crop_image_pil(
     image: PIL.Image.Image, size: List[int]
 ) -> Tuple[PIL.Image.Image, PIL.Image.Image, PIL.Image.Image, PIL.Image.Image, PIL.Image.Image]:
     crop_height, crop_width = _parse_five_crop_size(size)
-    _, image_height, image_width = get_dimensions_image_pil(image)
+    image_height, image_width = get_spatial_size_image_pil(image)
 
     if crop_width > image_width or crop_height > image_height:
         msg = "Requested crop size {} is bigger than input size {}"

From 9f964c35caefe973065ae80551f4c301135ff6f0 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 14:42:35 +0100
Subject: [PATCH 6/9] Fix linters

---
 torchvision/prototype/transforms/_utils.py           | 4 ++--
 torchvision/prototype/transforms/functional/_meta.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/torchvision/prototype/transforms/_utils.py b/torchvision/prototype/transforms/_utils.py
index 7db4f710481..4c17544afc8 100644
--- a/torchvision/prototype/transforms/_utils.py
+++ b/torchvision/prototype/transforms/_utils.py
@@ -1,7 +1,7 @@
 import numbers
 from collections import defaultdict
 
-from typing import Any, Callable, Dict, Sequence, Tuple, Type, Union
+from typing import Any, Callable, Dict, List, Sequence, Tuple, Type, Union
 
 import PIL.Image
 
@@ -77,7 +77,7 @@ def query_bounding_box(sample: Any) -> features.BoundingBox:
     return bounding_boxes.pop()
 
 
-def query_chw(sample: Any) -> Tuple[int, int, int]:
+def query_chw(sample: Any) -> List[int]:
     flat_sample, _ = tree_flatten(sample)
     chws = {
         get_dimensions(item)
diff --git a/torchvision/prototype/transforms/functional/_meta.py b/torchvision/prototype/transforms/functional/_meta.py
index f3fbdd73b2b..6a157a2056e 100644
--- a/torchvision/prototype/transforms/functional/_meta.py
+++ b/torchvision/prototype/transforms/functional/_meta.py
@@ -1,4 +1,4 @@
-from typing import cast, List, Optional, Tuple, Union
+from typing import cast, List, Optional, Tuple
 
 import PIL.Image
 import torch

From 77148495c8a2100e3248c94936508b0a19cabe0d Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 15:49:10 +0100
Subject: [PATCH 7/9] Fix merge bug

---
 torchvision/prototype/transforms/_auto_augment.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/torchvision/prototype/transforms/_auto_augment.py b/torchvision/prototype/transforms/_auto_augment.py
index add12e77366..ed8bfd3d3ee 100644
--- a/torchvision/prototype/transforms/_auto_augment.py
+++ b/torchvision/prototype/transforms/_auto_augment.py
@@ -472,9 +472,8 @@ def _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor:
 
     def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
-
-        id, image_or_video = self._extract_image_or_video(sample)
-        height, width = get_spatial_size(image_or_video)
+        id, orig_image_or_video = self._extract_image_or_video(sample)
+        height, width = get_spatial_size(orig_image_or_video)
 
         if isinstance(orig_image_or_video, torch.Tensor):
             image_or_video = orig_image_or_video

From f48c4f5e5c22b750d1ae526e3d23b53feee5c228 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 15:53:37 +0100
Subject: [PATCH 8/9] Linter

---
 torchvision/prototype/transforms/_utils.py           | 9 +++++----
 torchvision/prototype/transforms/functional/_meta.py | 1 -
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/torchvision/prototype/transforms/_utils.py b/torchvision/prototype/transforms/_utils.py
index 5ceec7e8b9c..db1ff4b7b6f 100644
--- a/torchvision/prototype/transforms/_utils.py
+++ b/torchvision/prototype/transforms/_utils.py
@@ -1,7 +1,7 @@
 import numbers
 from collections import defaultdict
 
-from typing import Any, Callable, Dict, List, Sequence, Tuple, Type, Union
+from typing import Any, Callable, Dict, Sequence, Tuple, Type, Union
 
 import PIL.Image
 
@@ -77,10 +77,10 @@ def query_bounding_box(sample: Any) -> features.BoundingBox:
     return bounding_boxes.pop()
 
 
-def query_chw(sample: Any) -> List[int]:
+def query_chw(sample: Any) -> Tuple[int, int, int]:
     flat_sample, _ = tree_flatten(sample)
     chws = {
-        get_dimensions(item)
+        tuple(get_dimensions(item))
         for item in flat_sample
         if isinstance(item, (features.Image, PIL.Image.Image, features.Video)) or features.is_simple_tensor(item)
     }
@@ -88,7 +88,8 @@ def query_chw(sample: Any) -> List[int]:
         raise TypeError("No image or video was found in the sample")
     elif len(chws) > 1:
         raise ValueError(f"Found multiple CxHxW dimensions in the sample: {sequence_to_str(sorted(chws))}")
-    return chws.pop()
+    c, h, w = chws.pop()
+    return c, h, w
 
 
 def _isinstance(obj: Any, types_or_checks: Tuple[Union[Type, Callable[[Any], bool]], ...]) -> bool:
diff --git a/torchvision/prototype/transforms/functional/_meta.py b/torchvision/prototype/transforms/functional/_meta.py
index f69711fcc33..e24b68c9fd6 100644
--- a/torchvision/prototype/transforms/functional/_meta.py
+++ b/torchvision/prototype/transforms/functional/_meta.py
@@ -68,7 +68,6 @@ def get_spatial_size(inpt: features.InputTypeJIT) -> List[int]:
         return get_spatial_size_image_pil(inpt)
 
 
-
 def _xywh_to_xyxy(xywh: torch.Tensor) -> torch.Tensor:
     xyxy = xywh.clone()
     xyxy[..., 2:] += xyxy[..., :2]

From 2a6ee3dc3b04f6c3d092c6d3a74e21eec68ad666 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Fri, 7 Oct 2022 16:57:37 +0100
Subject: [PATCH 9/9] Fix linter

---
 torchvision/prototype/features/_mask.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/torchvision/prototype/features/_mask.py b/torchvision/prototype/features/_mask.py
index 37c581c89a5..7b49ce8e85e 100644
--- a/torchvision/prototype/features/_mask.py
+++ b/torchvision/prototype/features/_mask.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+
 from typing import Any, cast, List, Optional, Tuple, Union
 
 import torch