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>

test/test_models_detection_negative_samples.py

@@ -4,7 +4,7 @@ import torchvision.models
 from common_utils import assert_equal
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
 from torchvision.models.detection.roi_heads import RoIHeads
-from torchvision.models.detection.rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
+from torchvision.models.detection.rpn import AnchorGenerator, RegionProposalNetwork, RPNHead
 from torchvision.ops import MultiScaleRoIAlign
 
 
@@ -60,7 +60,7 @@ class TestModelsDetectionNegativeSamples:
 
         resolution = box_roi_pool.output_size[0]
         representation_size = 1024
-        box_head = TwoMLPHead(4 * resolution ** 2, representation_size)
+        box_head = TwoMLPHead(4 * resolution**2, representation_size)
 
         representation_size = 1024
         box_predictor = FastRCNNPredictor(representation_size, 2)

test/test_onnx.py

@@ -4,13 +4,12 @@ from typing import List, Tuple
 
 import pytest
 import torch
-from common_utils import set_rng_seed, assert_equal
-from torchvision import models
-from torchvision import ops
+from common_utils import assert_equal, set_rng_seed
+from torchvision import models, ops
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
 from torchvision.models.detection.image_list import ImageList
 from torchvision.models.detection.roi_heads import RoIHeads
-from torchvision.models.detection.rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
+from torchvision.models.detection.rpn import AnchorGenerator, RegionProposalNetwork, RPNHead
 from torchvision.models.detection.transform import GeneralizedRCNNTransform
 from torchvision.ops._register_onnx_ops import _onnx_opset_version
 
@@ -265,7 +264,7 @@ class TestONNXExporter:
 
         resolution = box_roi_pool.output_size[0]
         representation_size = 1024
-        box_head = TwoMLPHead(out_channels * resolution ** 2, representation_size)
+        box_head = TwoMLPHead(out_channels * resolution**2, representation_size)
 
         representation_size = 1024
         box_predictor = FastRCNNPredictor(representation_size, num_classes)

test/test_ops.py

@@ -79,7 +79,7 @@ class RoIOpTester(ABC):
         rois_dtype = self.dtype if rois_dtype is None else rois_dtype
         pool_size = 5
         # n_channels % (pool_size ** 2) == 0 required for PS opeartions.
-        n_channels = 2 * (pool_size ** 2)
+        n_channels = 2 * (pool_size**2)
         x = torch.rand(2, n_channels, 10, 10, dtype=x_dtype, device=device)
         if not contiguous:
             x = x.permute(0, 1, 3, 2)
@@ -115,7 +115,7 @@ class RoIOpTester(ABC):
     def test_backward(self, seed, device, contiguous):
         torch.random.manual_seed(seed)
         pool_size = 2
-        x = torch.rand(1, 2 * (pool_size ** 2), 5, 5, dtype=self.dtype, device=device, requires_grad=True)
+        x = torch.rand(1, 2 * (pool_size**2), 5, 5, dtype=self.dtype, device=device, requires_grad=True)
         if not contiguous:
             x = x.permute(0, 1, 3, 2)
         rois = torch.tensor(

test/test_prototype_builtin_datasets.py

@@ -5,14 +5,14 @@ from pathlib import Path
 
 import pytest
 import torch
-from builtin_dataset_mocks import parametrize_dataset_mocks, DATASET_MOCKS
-from torch.testing._comparison import assert_equal, TensorLikePair, ObjectPair
+from builtin_dataset_mocks import DATASET_MOCKS, parametrize_dataset_mocks
+from torch.testing._comparison import assert_equal, ObjectPair, TensorLikePair
 from torch.utils.data import DataLoader
 from torch.utils.data.graph import traverse
 from torch.utils.data.graph_settings import get_all_graph_pipes
-from torchdata.datapipes.iter import Shuffler, ShardingFilter
+from torchdata.datapipes.iter import ShardingFilter, Shuffler
 from torchvision._utils import sequence_to_str
-from torchvision.prototype import transforms, datasets
+from torchvision.prototype import datasets, transforms
 from torchvision.prototype.datasets.utils._internal import INFINITE_BUFFER_SIZE
 from torchvision.prototype.features import Image, Label
 

test/test_prototype_datasets_utils.py

@@ -9,8 +9,8 @@ from datasets_utils import make_fake_flo_file, make_tar
 from torchdata.datapipes.iter import FileOpener, TarArchiveLoader
 from torchvision.datasets._optical_flow import _read_flo as read_flo_ref
 from torchvision.datasets.utils import _decompress
-from torchvision.prototype.datasets.utils import HttpResource, GDriveResource, Dataset, OnlineResource
-from torchvision.prototype.datasets.utils._internal import read_flo, fromfile
+from torchvision.prototype.datasets.utils import Dataset, GDriveResource, HttpResource, OnlineResource
+from torchvision.prototype.datasets.utils._internal import fromfile, read_flo
 
 
 @pytest.mark.filterwarnings("error:The given NumPy array is not writeable:UserWarning")

test/test_prototype_models.py

@@ -2,7 +2,7 @@ import pytest
 import test_models as TM
 import torch
 import torchvision.prototype.models.depth.stereo.raft_stereo as raft_stereo
-from common_utils import set_rng_seed, cpu_and_gpu
+from common_utils import cpu_and_gpu, set_rng_seed
 
 
 @pytest.mark.parametrize("model_builder", (raft_stereo.raft_stereo_base, raft_stereo.raft_stereo_realtime))

test/test_prototype_transforms.py

@@ -3,13 +3,9 @@ import itertools
 import pytest
 import torch
 from common_utils import assert_equal
-from test_prototype_transforms_functional import (
-    make_images,
-    make_bounding_boxes,
-    make_one_hot_labels,
-)
-from torchvision.prototype import transforms, features
-from torchvision.transforms.functional import to_pil_image, pil_to_tensor
+from test_prototype_transforms_functional import make_bounding_boxes, make_images, make_one_hot_labels
+from torchvision.prototype import features, transforms
+from torchvision.transforms.functional import pil_to_tensor, to_pil_image
 
 
 def make_vanilla_tensor_images(*args, **kwargs):

test/test_transforms.py

@@ -24,7 +24,7 @@ try:
 except ImportError:
     stats = None
 
-from common_utils import cycle_over, int_dtypes, float_dtypes, assert_equal
+from common_utils import assert_equal, cycle_over, float_dtypes, int_dtypes
 
 
 GRACE_HOPPER = get_file_path_2(

test/test_transforms_tensor.py

@@ -6,19 +6,18 @@ import pytest
 import torch
 import torchvision.transforms._pil_constants as _pil_constants
 from common_utils import (
-    get_tmp_dir,
-    int_dtypes,
-    float_dtypes,
+    _assert_approx_equal_tensor_to_pil,
+    _assert_equal_tensor_to_pil,
     _create_data,
     _create_data_batch,
-    _assert_equal_tensor_to_pil,
-    _assert_approx_equal_tensor_to_pil,
-    cpu_and_gpu,
     assert_equal,
+    cpu_and_gpu,
+    float_dtypes,
+    get_tmp_dir,
+    int_dtypes,
 )
 from torchvision import transforms as T
-from torchvision.transforms import InterpolationMode
-from torchvision.transforms import functional as F
+from torchvision.transforms import functional as F, InterpolationMode
 from torchvision.transforms.autoaugment import _apply_op
 
 NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC

test/test_utils.py

@@ -10,7 +10,7 @@ import torch
 import torchvision.transforms.functional as F
 import torchvision.utils as utils
 from common_utils import assert_equal
-from PIL import Image, __version__ as PILLOW_VERSION, ImageColor
+from PIL import __version__ as PILLOW_VERSION, Image, ImageColor
 
 
 PILLOW_VERSION = tuple(int(x) for x in PILLOW_VERSION.split("."))
@@ -45,8 +45,8 @@ def test_normalize_in_make_grid():
 
     # Rounding the result to one decimal for comparison
     n_digits = 1
-    rounded_grid_max = torch.round(grid_max * 10 ** n_digits) / (10 ** n_digits)
-    rounded_grid_min = torch.round(grid_min * 10 ** n_digits) / (10 ** n_digits)
+    rounded_grid_max = torch.round(grid_max * 10**n_digits) / (10**n_digits)
+    rounded_grid_min = torch.round(grid_min * 10**n_digits) / (10**n_digits)
 
     assert_equal(norm_max, rounded_grid_max, msg="Normalized max is not equal to 1")
     assert_equal(norm_min, rounded_grid_min, msg="Normalized min is not equal to 0")

torchvision/__init__.py

@@ -2,12 +2,7 @@ import os
 import warnings
 
 import torch
-from torchvision import datasets
-from torchvision import io
-from torchvision import models
-from torchvision import ops
-from torchvision import transforms
-from torchvision import utils
+from torchvision import datasets, io, models, ops, transforms, utils
 
 from .extension import _HAS_OPS
 

torchvision/_utils.py

 import enum
-from typing import Sequence, TypeVar, Type
+from typing import Sequence, Type, TypeVar
 
 T = TypeVar("T", bound=enum.Enum)
 

torchvision/datasets/__init__.py

-from ._optical_flow import KittiFlow, Sintel, FlyingChairs, FlyingThings3D, HD1K
+from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
 from .caltech import Caltech101, Caltech256
 from .celeba import CelebA
 from .cifar import CIFAR10, CIFAR100
@@ -11,19 +11,19 @@ from .eurosat import EuroSAT
 from .fakedata import FakeData
 from .fer2013 import FER2013
 from .fgvc_aircraft import FGVCAircraft
-from .flickr import Flickr8k, Flickr30k
+from .flickr import Flickr30k, Flickr8k
 from .flowers102 import Flowers102
-from .folder import ImageFolder, DatasetFolder
+from .folder import DatasetFolder, ImageFolder
 from .food101 import Food101
 from .gtsrb import GTSRB
 from .hmdb51 import HMDB51
 from .imagenet import ImageNet
 from .inaturalist import INaturalist
-from .kinetics import Kinetics400, Kinetics
+from .kinetics import Kinetics, Kinetics400
 from .kitti import Kitti
-from .lfw import LFWPeople, LFWPairs
+from .lfw import LFWPairs, LFWPeople
 from .lsun import LSUN, LSUNClass
-from .mnist import MNIST, EMNIST, FashionMNIST, KMNIST, QMNIST
+from .mnist import EMNIST, FashionMNIST, KMNIST, MNIST, QMNIST
 from .omniglot import Omniglot
 from .oxford_iiit_pet import OxfordIIITPet
 from .pcam import PCAM
@@ -40,7 +40,7 @@ from .svhn import SVHN
 from .ucf101 import UCF101
 from .usps import USPS
 from .vision import VisionDataset
-from .voc import VOCSegmentation, VOCDetection
+from .voc import VOCDetection, VOCSegmentation
 from .widerface import WIDERFace
 
 __all__ = (

torchvision/datasets/_optical_flow.py

@@ -9,7 +9,7 @@ import torch
 from PIL import Image
 
 from ..io.image import _read_png_16
-from .utils import verify_str_arg, _read_pfm
+from .utils import _read_pfm, verify_str_arg
 from .vision import VisionDataset
 
 
@@ -466,7 +466,7 @@ def _read_16bits_png_with_flow_and_valid_mask(file_name):
 
     flow_and_valid = _read_png_16(file_name).to(torch.float32)
     flow, valid_flow_mask = flow_and_valid[:2, :, :], flow_and_valid[2, :, :]
-    flow = (flow - 2 ** 15) / 64  # This conversion is explained somewhere on the kitti archive
+    flow = (flow - 2**15) / 64  # This conversion is explained somewhere on the kitti archive
     valid_flow_mask = valid_flow_mask.bool()
 
     # For consistency with other datasets, we convert to numpy

torchvision/datasets/caltech.py

 import os
 import os.path
-from typing import Any, Callable, List, Optional, Union, Tuple
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 from PIL import Image
 

torchvision/datasets/celeba.py

 import csv
 import os
 from collections import namedtuple
-from typing import Any, Callable, List, Optional, Union, Tuple
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import PIL
 import torch
 
-from .utils import download_file_from_google_drive, check_integrity, verify_str_arg, extract_archive
+from .utils import check_integrity, download_file_from_google_drive, extract_archive, verify_str_arg
 from .vision import VisionDataset
 
 CSV = namedtuple("CSV", ["header", "index", "data"])

torchvision/datasets/cityscapes.py

 import json
 import os
 from collections import namedtuple
-from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 from PIL import Image
 
-from .utils import extract_archive, verify_str_arg, iterable_to_str
+from .utils import extract_archive, iterable_to_str, verify_str_arg
 from .vision import VisionDataset
 
 

torchvision/datasets/clevr.py

 import json
 import pathlib
-from typing import Any, Callable, Optional, Tuple, List
+from typing import Any, Callable, List, Optional, Tuple
 from urllib.parse import urlparse
 
 from PIL import Image

torchvision/datasets/coco.py

 import os.path
-from typing import Any, Callable, Optional, Tuple, List
+from typing import Any, Callable, List, Optional, Tuple
 
 from PIL import Image
 

torchvision/datasets/country211.py

@@ -2,7 +2,7 @@ from pathlib import Path
 from typing import Callable, Optional
 
 from .folder import ImageFolder
-from .utils import verify_str_arg, download_and_extract_archive
+from .utils import download_and_extract_archive, verify_str_arg
 
 
 class Country211(ImageFolder):