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/ops/boxes.py

@@ -6,7 +6,7 @@ from torch import Tensor
 from torchvision.extension import _assert_has_ops
 
 from ..utils import _log_api_usage_once
-from ._box_convert import _box_cxcywh_to_xyxy, _box_xyxy_to_cxcywh, _box_xywh_to_xyxy, _box_xyxy_to_xywh
+from ._box_convert import _box_cxcywh_to_xyxy, _box_xywh_to_xyxy, _box_xyxy_to_cxcywh, _box_xyxy_to_xywh
 from ._utils import _upcast
 
 
@@ -331,7 +331,7 @@ def complete_box_iou(boxes1: Tensor, boxes2: Tensor, eps: float = 1e-7) -> Tenso
     w_gt = boxes2[:, 2] - boxes2[:, 0]
     h_gt = boxes2[:, 3] - boxes2[:, 1]
 
-    v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
+    v = (4 / (torch.pi**2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
     with torch.no_grad():
         alpha = v / (1 - iou + v + eps)
     return diou - alpha * v

torchvision/ops/ciou_loss.py

@@ -58,7 +58,7 @@ def complete_box_iou_loss(
     h_pred = y2 - y1
     w_gt = x2g - x1g
     h_gt = y2g - y1g
-    v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
+    v = (4 / (torch.pi**2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
     with torch.no_grad():
         alpha = v / (1 - iou + v + eps)
 

torchvision/ops/drop_block.py

@@ -37,7 +37,7 @@ def drop_block2d(
     N, C, H, W = input.size()
     block_size = min(block_size, W, H)
     # compute the gamma of Bernoulli distribution
-    gamma = (p * H * W) / ((block_size ** 2) * ((H - block_size + 1) * (W - block_size + 1)))
+    gamma = (p * H * W) / ((block_size**2) * ((H - block_size + 1) * (W - block_size + 1)))
     noise = torch.empty((N, C, H - block_size + 1, W - block_size + 1), dtype=input.dtype, device=input.device)
     noise.bernoulli_(gamma)
 
@@ -83,7 +83,7 @@ def drop_block3d(
     N, C, D, H, W = input.size()
     block_size = min(block_size, D, H, W)
     # compute the gamma of Bernoulli distribution
-    gamma = (p * D * H * W) / ((block_size ** 3) * ((D - block_size + 1) * (H - block_size + 1) * (W - block_size + 1)))
+    gamma = (p * D * H * W) / ((block_size**3) * ((D - block_size + 1) * (H - block_size + 1) * (W - block_size + 1)))
     noise = torch.empty(
         (N, C, D - block_size + 1, H - block_size + 1, W - block_size + 1), dtype=input.dtype, device=input.device
     )

torchvision/ops/feature_pyramid_network.py

 from collections import OrderedDict
-from typing import Tuple, List, Dict, Callable, Optional
+from typing import Callable, Dict, List, Optional, Tuple
 
 import torch.nn.functional as F
 from torch import nn, Tensor

torchvision/ops/giou_loss.py

 import torch
 
 from ..utils import _log_api_usage_once
-from ._utils import _upcast_non_float, _loss_inter_union
+from ._utils import _loss_inter_union, _upcast_non_float
 
 
 def generalized_box_iou_loss(

torchvision/ops/misc.py

 import warnings
-from typing import Callable, List, Optional, Union, Tuple, Sequence
+from typing import Callable, List, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor

torchvision/ops/poolers.py

 import warnings
-from typing import Optional, List, Dict, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.fx

torchvision/ops/ps_roi_align.py

@@ -4,7 +4,7 @@ from torch.nn.modules.utils import _pair
 from torchvision.extension import _assert_has_ops
 
 from ..utils import _log_api_usage_once
-from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
+from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
 
 
 def ps_roi_align(

torchvision/ops/ps_roi_pool.py

@@ -4,7 +4,7 @@ from torch.nn.modules.utils import _pair
 from torchvision.extension import _assert_has_ops
 
 from ..utils import _log_api_usage_once
-from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
+from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
 
 
 def ps_roi_pool(

torchvision/ops/roi_align.py

@@ -7,7 +7,7 @@ from torch.nn.modules.utils import _pair
 from torchvision.extension import _assert_has_ops
 
 from ..utils import _log_api_usage_once
-from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
+from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
 
 
 def roi_align(

torchvision/ops/roi_pool.py

@@ -7,7 +7,7 @@ from torch.nn.modules.utils import _pair
 from torchvision.extension import _assert_has_ops
 
 from ..utils import _log_api_usage_once
-from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
+from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
 
 
 def roi_pool(

torchvision/prototype/__init__.py

-from . import datasets
-from . import features
-from . import models
-from . import transforms
-from . import utils
+from . import datasets, features, models, transforms, utils

torchvision/prototype/datasets/_api.py

 import pathlib
-from typing import Any, Dict, List, Callable, Type, Optional, Union, TypeVar
+from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union
 
 from torchvision.prototype.datasets import home
 from torchvision.prototype.datasets.utils import Dataset

torchvision/prototype/datasets/_builtin/__init__.py

@@ -11,7 +11,7 @@ from .fer2013 import FER2013
 from .food101 import Food101
 from .gtsrb import GTSRB
 from .imagenet import ImageNet
-from .mnist import MNIST, FashionMNIST, KMNIST, EMNIST, QMNIST
+from .mnist import EMNIST, FashionMNIST, KMNIST, MNIST, QMNIST
 from .oxford_iiit_pet import OxfordIIITPet
 from .pcam import PCAM
 from .sbd import SBD

torchvision/prototype/datasets/_builtin/caltech.py

 import pathlib
 import re
-from typing import Any, Dict, List, Tuple, BinaryIO, Union
+from typing import Any, BinaryIO, Dict, List, Tuple, Union
 
 import numpy as np
-from torchdata.datapipes.iter import (
-    IterDataPipe,
-    Mapper,
-    Filter,
-    IterKeyZipper,
-)
+from torchdata.datapipes.iter import Filter, IterDataPipe, IterKeyZipper, Mapper
 from torchvision.prototype.datasets.utils import Dataset, GDriveResource, OnlineResource
 from torchvision.prototype.datasets.utils._internal import (
-    INFINITE_BUFFER_SIZE,
-    read_mat,
     hint_sharding,
     hint_shuffling,
+    INFINITE_BUFFER_SIZE,
     read_categories_file,
+    read_mat,
 )
-from torchvision.prototype.features import Label, BoundingBox, _Feature, EncodedImage
+from torchvision.prototype.features import _Feature, BoundingBox, EncodedImage, Label
 
 from .._api import register_dataset, register_info
 

torchvision/prototype/datasets/_builtin/celeba.py

 import csv
 import pathlib
-from typing import Any, Dict, List, Optional, Tuple, Iterator, Sequence, BinaryIO, Union
-
-from torchdata.datapipes.iter import (
-    IterDataPipe,
-    Mapper,
-    Filter,
-    Zipper,
-    IterKeyZipper,
-)
-from torchvision.prototype.datasets.utils import (
-    Dataset,
-    GDriveResource,
-    OnlineResource,
-)
+from typing import Any, BinaryIO, Dict, Iterator, List, Optional, Sequence, Tuple, Union
+
+from torchdata.datapipes.iter import Filter, IterDataPipe, IterKeyZipper, Mapper, Zipper
+from torchvision.prototype.datasets.utils import Dataset, GDriveResource, OnlineResource
 from torchvision.prototype.datasets.utils._internal import (
-    INFINITE_BUFFER_SIZE,
     getitem,
-    path_accessor,
     hint_sharding,
     hint_shuffling,
+    INFINITE_BUFFER_SIZE,
+    path_accessor,
 )
-from torchvision.prototype.features import EncodedImage, _Feature, Label, BoundingBox
+from torchvision.prototype.features import _Feature, BoundingBox, EncodedImage, Label
 
 from .._api import register_dataset, register_info
 

torchvision/prototype/datasets/_builtin/cifar.py

@@ -2,22 +2,18 @@ import abc
 import io
 import pathlib
 import pickle
-from typing import Any, Dict, List, Optional, Tuple, Iterator, cast, BinaryIO, Union
+from typing import Any, BinaryIO, cast, Dict, Iterator, List, Optional, Tuple, Union
 
 import numpy as np
-from torchdata.datapipes.iter import (
-    IterDataPipe,
-    Filter,
-    Mapper,
-)
+from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
 from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
 from torchvision.prototype.datasets.utils._internal import (
+    hint_sharding,
     hint_shuffling,
     path_comparator,
-    hint_sharding,
     read_categories_file,
 )
-from torchvision.prototype.features import Label, Image
+from torchvision.prototype.features import Image, Label
 
 from .._api import register_dataset, register_info
 

torchvision/prototype/datasets/_builtin/clevr.py

 import pathlib
-from typing import Any, Dict, List, Optional, Tuple, BinaryIO, Union
+from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
 
-from torchdata.datapipes.iter import IterDataPipe, Mapper, Filter, IterKeyZipper, Demultiplexer, JsonParser, UnBatcher
+from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
 from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
 from torchvision.prototype.datasets.utils._internal import (
-    INFINITE_BUFFER_SIZE,
+    getitem,
     hint_sharding,
     hint_shuffling,
-    path_comparator,
+    INFINITE_BUFFER_SIZE,
     path_accessor,
-    getitem,
+    path_comparator,
 )
-from torchvision.prototype.features import Label, EncodedImage
+from torchvision.prototype.features import EncodedImage, Label
 
 from .._api import register_dataset, register_info
 

torchvision/prototype/datasets/_builtin/coco.py

 import pathlib
 import re
-from collections import OrderedDict
-from collections import defaultdict
-from typing import Any, Dict, List, Optional, Tuple, cast, BinaryIO, Union
+from collections import defaultdict, OrderedDict
+from typing import Any, BinaryIO, cast, Dict, List, Optional, Tuple, Union
 
 import torch
 from torchdata.datapipes.iter import (
-    IterDataPipe,
-    Mapper,
-    Filter,
     Demultiplexer,
+    Filter,
     Grouper,
+    IterDataPipe,
     IterKeyZipper,
     JsonParser,
+    Mapper,
     UnBatcher,
 )
-from torchvision.prototype.datasets.utils import (
-    HttpResource,
-    OnlineResource,
-    Dataset,
-)
+from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
 from torchvision.prototype.datasets.utils._internal import (
-    MappingIterator,
-    INFINITE_BUFFER_SIZE,
     getitem,
-    read_categories_file,
-    path_accessor,
     hint_sharding,
     hint_shuffling,
+    INFINITE_BUFFER_SIZE,
+    MappingIterator,
+    path_accessor,
+    read_categories_file,
 )
-from torchvision.prototype.features import BoundingBox, Label, _Feature, EncodedImage
+from torchvision.prototype.features import _Feature, BoundingBox, EncodedImage, Label
 
 from .._api import register_dataset, register_info
 
@@ -151,7 +146,7 @@ class Coco(Dataset):
     )
 
     _META_FILE_PATTERN = re.compile(
-        fr"(?P<annotations>({'|'.join(_ANN_DECODERS.keys())}))_(?P<split>[a-zA-Z]+)(?P<year>\d+)[.]json"
+        rf"(?P<annotations>({'|'.join(_ANN_DECODERS.keys())}))_(?P<split>[a-zA-Z]+)(?P<year>\d+)[.]json"
     )
 
     def _filter_meta_files(self, data: Tuple[str, Any]) -> bool:

torchvision/prototype/datasets/_builtin/country211.py

 import pathlib
 from typing import Any, Dict, List, Tuple, Union
 
-from torchdata.datapipes.iter import IterDataPipe, Mapper, Filter
+from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
 from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
 from torchvision.prototype.datasets.utils._internal import (
-    path_comparator,
     hint_sharding,
     hint_shuffling,
+    path_comparator,
     read_categories_file,
 )
 from torchvision.prototype.features import EncodedImage, Label