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>

.pre-commit-config.yaml

@@ -15,8 +15,8 @@ repos:
     hooks:
       - id: ufmt
         additional_dependencies:
-          - black == 21.9b0
-          - usort == 0.6.4
+          - black == 22.3.0
+          - usort == 1.0.2
 
   - repo: https://gitlab.com/pycqa/flake8
     rev: 3.9.2

hubconf.py

@@ -3,8 +3,8 @@ dependencies = ["torch"]
 
 from torchvision.models import get_weight
 from torchvision.models.alexnet import alexnet
-from torchvision.models.convnext import convnext_tiny, convnext_small, convnext_base, convnext_large
-from torchvision.models.densenet import densenet121, densenet169, densenet201, densenet161
+from torchvision.models.convnext import convnext_base, convnext_large, convnext_small, convnext_tiny
+from torchvision.models.densenet import densenet121, densenet161, densenet169, densenet201
 from torchvision.models.efficientnet import (
     efficientnet_b0,
     efficientnet_b1,
@@ -14,9 +14,9 @@ from torchvision.models.efficientnet import (
     efficientnet_b5,
     efficientnet_b6,
     efficientnet_b7,
-    efficientnet_v2_s,
-    efficientnet_v2_m,
     efficientnet_v2_l,
+    efficientnet_v2_m,
+    efficientnet_v2_s,
 )
 from torchvision.models.googlenet import googlenet
 from torchvision.models.inception import inception_v3
@@ -25,40 +25,40 @@ from torchvision.models.mobilenetv2 import mobilenet_v2
 from torchvision.models.mobilenetv3 import mobilenet_v3_large, mobilenet_v3_small
 from torchvision.models.optical_flow import raft_large, raft_small
 from torchvision.models.regnet import (
-    regnet_y_400mf,
-    regnet_y_800mf,
-    regnet_y_1_6gf,
-    regnet_y_3_2gf,
-    regnet_y_8gf,
-    regnet_y_16gf,
-    regnet_y_32gf,
-    regnet_y_128gf,
-    regnet_x_400mf,
-    regnet_x_800mf,
+    regnet_x_16gf,
     regnet_x_1_6gf,
+    regnet_x_32gf,
     regnet_x_3_2gf,
+    regnet_x_400mf,
+    regnet_x_800mf,
     regnet_x_8gf,
-    regnet_x_16gf,
-    regnet_x_32gf,
+    regnet_y_128gf,
+    regnet_y_16gf,
+    regnet_y_1_6gf,
+    regnet_y_32gf,
+    regnet_y_3_2gf,
+    regnet_y_400mf,
+    regnet_y_800mf,
+    regnet_y_8gf,
 )
 from torchvision.models.resnet import (
+    resnet101,
+    resnet152,
     resnet18,
     resnet34,
     resnet50,
-    resnet101,
-    resnet152,
-    resnext50_32x4d,
     resnext101_32x8d,
     resnext101_64x4d,
-    wide_resnet50_2,
+    resnext50_32x4d,
     wide_resnet101_2,
+    wide_resnet50_2,
 )
 from torchvision.models.segmentation import (
-    fcn_resnet50,
-    fcn_resnet101,
-    deeplabv3_resnet50,
-    deeplabv3_resnet101,
     deeplabv3_mobilenet_v3_large,
+    deeplabv3_resnet101,
+    deeplabv3_resnet50,
+    fcn_resnet101,
+    fcn_resnet50,
     lraspp_mobilenet_v3_large,
 )
 from torchvision.models.shufflenetv2 import (
@@ -68,12 +68,6 @@ from torchvision.models.shufflenetv2 import (
     shufflenet_v2_x2_0,
 )
 from torchvision.models.squeezenet import squeezenet1_0, squeezenet1_1
-from torchvision.models.swin_transformer import swin_t, swin_s, swin_b
-from torchvision.models.vgg import vgg11, vgg13, vgg16, vgg19, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn
-from torchvision.models.vision_transformer import (
-    vit_b_16,
-    vit_b_32,
-    vit_l_16,
-    vit_l_32,
-    vit_h_14,
-)
+from torchvision.models.swin_transformer import swin_b, swin_s, swin_t
+from torchvision.models.vgg import vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn
+from torchvision.models.vision_transformer import vit_b_16, vit_b_32, vit_h_14, vit_l_16, vit_l_32

references/classification/train_quantization.py

@@ -9,7 +9,7 @@ import torch.utils.data
 import torchvision
 import utils
 from torch import nn
-from train import train_one_epoch, evaluate, load_data
+from train import evaluate, load_data, train_one_epoch
 
 
 def main(args):

references/detection/group_by_aspect_ratio.py

@@ -2,7 +2,7 @@ import bisect
 import copy
 import math
 from collections import defaultdict
-from itertools import repeat, chain
+from itertools import chain, repeat
 
 import numpy as np
 import torch

references/detection/train.py

@@ -29,8 +29,8 @@ import torchvision.models.detection
 import torchvision.models.detection.mask_rcnn
 import utils
 from coco_utils import get_coco, get_coco_kp
-from engine import train_one_epoch, evaluate
-from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups
+from engine import evaluate, train_one_epoch
+from group_by_aspect_ratio import create_aspect_ratio_groups, GroupedBatchSampler
 from torchvision.transforms import InterpolationMode
 from transforms import SimpleCopyPaste
 

references/detection/transforms.py

-from typing import List, Tuple, Dict, Optional, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torchvision
 from torch import nn, Tensor
 from torchvision import ops
-from torchvision.transforms import functional as F
-from torchvision.transforms import transforms as T, InterpolationMode
+from torchvision.transforms import functional as F, InterpolationMode, transforms as T
 
 
 def _flip_coco_person_keypoints(kps, width):

references/optical_flow/train.py

@@ -6,8 +6,8 @@ from pathlib import Path
 import torch
 import torchvision.models.optical_flow
 import utils
-from presets import OpticalFlowPresetTrain, OpticalFlowPresetEval
-from torchvision.datasets import KittiFlow, FlyingChairs, FlyingThings3D, Sintel, HD1K
+from presets import OpticalFlowPresetEval, OpticalFlowPresetTrain
+from torchvision.datasets import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
 
 
 def get_train_dataset(stage, dataset_root):

references/optical_flow/utils.py

 import datetime
 import os
 import time
-from collections import defaultdict
-from collections import deque
+from collections import defaultdict, deque
 
 import torch
 import torch.distributed as dist
@@ -158,7 +157,7 @@ class MetricLogger:
 def compute_metrics(flow_pred, flow_gt, valid_flow_mask=None):
 
     epe = ((flow_pred - flow_gt) ** 2).sum(dim=1).sqrt()
-    flow_norm = (flow_gt ** 2).sum(dim=1).sqrt()
+    flow_norm = (flow_gt**2).sum(dim=1).sqrt()
 
     if valid_flow_mask is not None:
         epe = epe[valid_flow_mask]
@@ -183,7 +182,7 @@ def sequence_loss(flow_preds, flow_gt, valid_flow_mask, gamma=0.8, max_flow=400)
         raise ValueError(f"Gamma should be < 1, got {gamma}.")
 
     # exlude invalid pixels and extremely large diplacements
-    flow_norm = torch.sum(flow_gt ** 2, dim=1).sqrt()
+    flow_norm = torch.sum(flow_gt**2, dim=1).sqrt()
     valid_flow_mask = valid_flow_mask & (flow_norm < max_flow)
 
     valid_flow_mask = valid_flow_mask[:, None, :, :]

references/segmentation/utils.py

@@ -75,7 +75,7 @@ class ConfusionMatrix:
         with torch.inference_mode():
             k = (a >= 0) & (a < n)
             inds = n * a[k].to(torch.int64) + b[k]
-            self.mat += torch.bincount(inds, minlength=n ** 2).reshape(n, n)
+            self.mat += torch.bincount(inds, minlength=n**2).reshape(n, n)
 
     def reset(self):
         self.mat.zero_()

setup.py

@@ -7,9 +7,9 @@ import subprocess
 import sys
 
 import torch
-from pkg_resources import parse_version, get_distribution, DistributionNotFound
-from setuptools import setup, find_packages
-from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
+from pkg_resources import DistributionNotFound, get_distribution, parse_version
+from setuptools import find_packages, setup
+from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDA_HOME, CUDAExtension
 
 
 def read(*names, **kwargs):

test/builtin_dataset_mocks.py

@@ -14,12 +14,12 @@ import shutil
 import unittest.mock
 import warnings
 import xml.etree.ElementTree as ET
-from collections import defaultdict, Counter
+from collections import Counter, defaultdict
 
 import numpy as np
 import pytest
 import torch
-from datasets_utils import make_zip, make_tar, create_image_folder, create_image_file, combinations_grid
+from datasets_utils import combinations_grid, create_image_file, create_image_folder, make_tar, make_zip
 from torch.nn.functional import one_hot
 from torch.testing import make_tensor as _make_tensor
 from torchvision.prototype import datasets

test/conftest.py

@@ -3,7 +3,7 @@ import random
 import numpy as np
 import pytest
 import torch
-from common_utils import IN_CIRCLE_CI, CIRCLECI_GPU_NO_CUDA_MSG, IN_FBCODE, IN_RE_WORKER, CUDA_NOT_AVAILABLE_MSG
+from common_utils import CIRCLECI_GPU_NO_CUDA_MSG, CUDA_NOT_AVAILABLE_MSG, IN_CIRCLE_CI, IN_FBCODE, IN_RE_WORKER
 
 
 def pytest_configure(config):

test/datasets_utils.py

@@ -22,7 +22,7 @@ import pytest
 import torch
 import torchvision.datasets
 import torchvision.io
-from common_utils import get_tmp_dir, disable_console_output
+from common_utils import disable_console_output, get_tmp_dir
 
 
 __all__ = [

test/test_datasets_download.py

@@ -9,15 +9,15 @@ from distutils import dir_util
 from os import path
 from urllib.error import HTTPError, URLError
 from urllib.parse import urlparse
-from urllib.request import urlopen, Request
+from urllib.request import Request, urlopen
 
 import pytest
 from torchvision import datasets
 from torchvision.datasets.utils import (
-    download_url,
+    _get_redirect_url,
     check_integrity,
     download_file_from_google_drive,
-    _get_redirect_url,
+    download_url,
     USER_AGENT,
 )
 

test/test_datasets_samplers.py

 import pytest
 import torch
-from common_utils import get_list_of_videos, assert_equal
+from common_utils import assert_equal, get_list_of_videos
 from torchvision import io
-from torchvision.datasets.samplers import (
-    DistributedSampler,
-    RandomClipSampler,
-    UniformClipSampler,
-)
+from torchvision.datasets.samplers import DistributedSampler, RandomClipSampler, UniformClipSampler
 from torchvision.datasets.video_utils import VideoClips
 
 

test/test_datasets_video_utils.py

 import pytest
 import torch
-from common_utils import get_list_of_videos, assert_equal
+from common_utils import assert_equal, get_list_of_videos
 from torchvision import io
-from torchvision.datasets.video_utils import VideoClips, unfold
+from torchvision.datasets.video_utils import unfold, VideoClips
 
 
 class TestVideo:

test/test_extended_models.py

@@ -5,7 +5,7 @@ import pytest
 import test_models as TM
 import torch
 from torchvision import models
-from torchvision.models._api import WeightsEnum, Weights
+from torchvision.models._api import Weights, WeightsEnum
 from torchvision.models._utils import handle_legacy_interface
 
 

test/test_functional_tensor.py

@@ -14,14 +14,14 @@ import torchvision.transforms.functional as F
 import torchvision.transforms.functional_pil as F_pil
 import torchvision.transforms.functional_tensor as F_t
 from common_utils import (
-    cpu_and_gpu,
-    needs_cuda,
+    _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,
     _test_fn_on_batch,
     assert_equal,
+    cpu_and_gpu,
+    needs_cuda,
 )
 from torchvision.transforms import InterpolationMode
 

test/test_image.py

@@ -8,21 +8,21 @@ import numpy as np
 import pytest
 import torch
 import torchvision.transforms.functional as F
-from common_utils import needs_cuda, assert_equal
-from PIL import Image, __version__ as PILLOW_VERSION
+from common_utils import assert_equal, needs_cuda
+from PIL import __version__ as PILLOW_VERSION, Image
 from torchvision.io.image import (
-    decode_png,
+    _read_png_16,
+    decode_image,
     decode_jpeg,
+    decode_png,
     encode_jpeg,
-    write_jpeg,
-    decode_image,
-    read_file,
     encode_png,
-    write_png,
-    write_file,
     ImageReadMode,
+    read_file,
     read_image,
-    _read_png_16,
+    write_file,
+    write_jpeg,
+    write_png,
 )
 
 IMAGE_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")
@@ -168,7 +168,7 @@ def test_decode_png(img_path, pil_mode, mode):
         img_lpng = _read_png_16(img_path, mode=mode)
         assert img_lpng.dtype == torch.int32
         # PIL converts 16 bits pngs in uint8
-        img_lpng = torch.round(img_lpng / (2 ** 16 - 1) * 255).to(torch.uint8)
+        img_lpng = torch.round(img_lpng / (2**16 - 1) * 255).to(torch.uint8)
     else:
         data = read_file(img_path)
         img_lpng = decode_image(data, mode=mode)

test/test_models.py

@@ -14,7 +14,7 @@ import torch
 import torch.fx
 import torch.nn as nn
 from _utils_internal import get_relative_path
-from common_utils import map_nested_tensor_object, freeze_rng_state, set_rng_seed, cpu_and_gpu, needs_cuda
+from common_utils import cpu_and_gpu, freeze_rng_state, map_nested_tensor_object, needs_cuda, set_rng_seed
 from torchvision import models
 
 ACCEPT = os.getenv("EXPECTTEST_ACCEPT", "0") == "1"