Upgrade usort to `1.0.2` and black to 22.3.0 (#5106)

* upgrade usort to

* Also update black

* Actually use 1.0.2

* Apply pre-commit
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

torchvision/prototype/transforms/functional/_meta.py

-from typing import Tuple, Optional
+from typing import Optional, Tuple
 
 import PIL.Image
 import torch
 from torchvision.prototype.features import BoundingBoxFormat, ColorSpace
-from torchvision.transforms import functional_tensor as _FT, functional_pil as _FP
+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

torchvision/prototype/transforms/functional/_misc.py

-from typing import Optional, List
+from typing import List, Optional
 
 import PIL.Image
 import torch

torchvision/prototype/transforms/functional/_type_conversion.py

 import unittest.mock
-from typing import Dict, Any, Tuple, Union
+from typing import Any, Dict, Tuple, Union
 
 import numpy as np
 import PIL.Image

torchvision/prototype/utils/_internal.py

@@ -3,18 +3,7 @@ import difflib
 import io
 import mmap
 import platform
-from typing import (
-    Any,
-    BinaryIO,
-    Callable,
-    Collection,
-    Iterator,
-    Sequence,
-    Tuple,
-    TypeVar,
-    Union,
-    Optional,
-)
+from typing import Any, BinaryIO, Callable, Collection, Iterator, Optional, Sequence, Tuple, TypeVar, Union
 
 import numpy as np
 import torch

torchvision/transforms/_presets.py

@@ -5,7 +5,7 @@ future versions without warning. The classes should be accessed only via the tra
 from typing import Optional, Tuple
 
 import torch
-from torch import Tensor, nn
+from torch import nn, Tensor
 
 from . import functional as F, InterpolationMode
 

torchvision/transforms/_transforms_video.py

@@ -4,10 +4,7 @@ import numbers
 import random
 import warnings
 
-from torchvision.transforms import (
-    RandomCrop,
-    RandomResizedCrop,
-)
+from torchvision.transforms import RandomCrop, RandomResizedCrop
 
 from . import _functional_video as F
 

torchvision/transforms/autoaugment.py

 import math
 from enum import Enum
-from typing import List, Tuple, Optional, Dict
+from typing import Dict, List, Optional, Tuple
 
 import torch
 from torch import Tensor

torchvision/transforms/functional.py

@@ -2,7 +2,7 @@ import math
 import numbers
 import warnings
 from enum import Enum
-from typing import List, Tuple, Any, Optional, Union
+from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -15,8 +15,7 @@ except ImportError:
     accimage = None
 
 from ..utils import _log_api_usage_once
-from . import functional_pil as F_pil
-from . import functional_tensor as F_t
+from . import functional_pil as F_pil, functional_tensor as F_t
 
 
 class InterpolationMode(Enum):

torchvision/transforms/functional_tensor.py

 import warnings
-from typing import Optional, Tuple, List, Union
+from typing import List, Optional, Tuple, Union
 
 import torch
 from torch import Tensor
-from torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad
+from torch.nn.functional import conv2d, grid_sample, interpolate, pad as torch_pad
 
 
 def _is_tensor_a_torch_image(x: Tensor) -> bool:
@@ -247,7 +247,7 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
     if not torch.is_floating_point(img):
         result = convert_image_dtype(result, torch.float32)
 
-    result = (gain * result ** gamma).clamp(0, 1)
+    result = (gain * result**gamma).clamp(0, 1)
 
     result = convert_image_dtype(result, dtype)
     return result

torchvision/transforms/transforms.py

@@ -3,7 +3,7 @@ import numbers
 import random
 import warnings
 from collections.abc import Sequence
-from typing import Tuple, List, Optional
+from typing import List, Optional, Tuple
 
 import torch
 from torch import Tensor
@@ -15,7 +15,7 @@ except ImportError:
 
 from ..utils import _log_api_usage_once
 from . import functional as F
-from .functional import InterpolationMode, _interpolation_modes_from_int
+from .functional import _interpolation_modes_from_int, InterpolationMode
 
 __all__ = [
     "Compose",

torchvision/utils.py

@@ -449,7 +449,7 @@ def flow_to_image(flow: torch.Tensor) -> torch.Tensor:
     if flow.ndim != 4 or flow.shape[1] != 2:
         raise ValueError(f"Input flow should have shape (2, H, W) or (N, 2, H, W), got {orig_shape}.")
 
-    max_norm = torch.sum(flow ** 2, dim=1).sqrt().max()
+    max_norm = torch.sum(flow**2, dim=1).sqrt().max()
     epsilon = torch.finfo((flow).dtype).eps
     normalized_flow = flow / (max_norm + epsilon)
     img = _normalized_flow_to_image(normalized_flow)
@@ -476,7 +476,7 @@ def _normalized_flow_to_image(normalized_flow: torch.Tensor) -> torch.Tensor:
     flow_image = torch.zeros((N, 3, H, W), dtype=torch.uint8, device=device)
     colorwheel = _make_colorwheel().to(device)  # shape [55x3]
     num_cols = colorwheel.shape[0]
-    norm = torch.sum(normalized_flow ** 2, dim=1).sqrt()
+    norm = torch.sum(normalized_flow**2, dim=1).sqrt()
     a = torch.atan2(-normalized_flow[:, 1, :, :], -normalized_flow[:, 0, :, :]) / torch.pi
     fk = (a + 1) / 2 * (num_cols - 1)
     k0 = torch.floor(fk).to(torch.long)
@@ -542,7 +542,7 @@ def _make_colorwheel() -> torch.Tensor:
 
 
 def _generate_color_palette(num_objects: int):
-    palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
+    palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1])
     return [tuple((i * palette) % 255) for i in range(num_objects)]