precommit: isort (#5493)

* precommit: isort

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update isort config

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update name

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

.pre-commit-config.yaml

         args: [--py36-plus]
         name: Upgrade code
 
-  # TODO
-  #- repo: https://github.com/PyCQA/isort
-  #  rev: 5.9.3
-  #  hooks:
-  #    - id: isort
-  #      name: imports
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.9.3
+    hooks:
+      - id: isort
+        name: Sort imports
 
   # TODO
   #- repo: https://github.com/pre-commit/mirrors-yapf

detect.py

 
 from models.experimental import attempt_load
 from utils.datasets import LoadImages, LoadStreams
-from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
-    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, strip_optimizer, xyxy2xywh, LOGGER
+from utils.general import (LOGGER, apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix,
+                           colorstr, increment_path, non_max_suppression, print_args, save_one_box, scale_coords,
+                           strip_optimizer, xyxy2xywh)
 from utils.plots import Annotator, colors
 from utils.torch_utils import load_classifier, select_device, time_sync
 

export.py

 from models.yolo import Detect
 from utils.activations import SiLU
 from utils.datasets import LoadImages
-from utils.general import check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args, \
-    url2file, LOGGER
+from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args,
+                           url2file)
 from utils.torch_utils import select_device
 
 
     try:
         import tensorflow as tf
         from tensorflow import keras
-        from models.tf import TFModel, TFDetect
+
+        from models.tf import TFDetect, TFModel
 
         LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         f = str(file).replace('.pt', '_saved_model')
     # YOLOv5 TensorFlow Lite export
     try:
         import tensorflow as tf
+
         from models.tf import representative_dataset_gen
 
         LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
     try:
         check_requirements(('tensorflowjs',))
         import re
+
         import tensorflowjs as tfjs
 
         LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')

hubconf.py

     """
     from pathlib import Path
 
-    from models.yolo import Model
     from models.experimental import attempt_load
-    from utils.general import check_requirements, set_logging
+    from models.yolo import Model
     from utils.downloads import attempt_download
+    from utils.general import check_requirements, set_logging
     from utils.torch_utils import select_device
 
     file = Path(__file__).resolve()
     # model = custom(path='path/to/model.pt')  # custom
 
     # Verify inference
+    from pathlib import Path
+
     import cv2
     import numpy as np
     from PIL import Image
-    from pathlib import Path
 
     imgs = ['data/images/zidane.jpg',  # filename
             Path('data/images/zidane.jpg'),  # Path

models/common.py

 from torch.cuda import amp
 
 from utils.datasets import exif_transpose, letterbox
-from utils.general import colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, \
-    scale_coords, xyxy2xywh
+from utils.general import (colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, scale_coords,
+                           xyxy2xywh)
 from utils.plots import Annotator, colors
 from utils.torch_utils import time_sync
 

models/experimental.py

 Experimental modules
 """
 import math
+
 import numpy as np
 import torch
 import torch.nn as nn

models/tf.py

 import torch.nn as nn
 from tensorflow import keras
 
-from models.common import Bottleneck, BottleneckCSP, Concat, Conv, C3, DWConv, Focus, SPP, SPPF, autopad
+from models.common import C3, SPP, SPPF, Bottleneck, BottleneckCSP, Concat, Conv, DWConv, Focus, autopad
 from models.experimental import CrossConv, MixConv2d, attempt_load
 from models.yolo import Detect
-from utils.general import make_divisible, print_args, LOGGER
 from utils.activations import SiLU
+from utils.general import LOGGER, make_divisible, print_args
 
 
 class TFBN(keras.layers.Layer):

models/yolo.py

 from models.common import *
 from models.experimental import *
 from utils.autoanchor import check_anchor_order
-from utils.general import check_version, check_yaml, make_divisible, print_args, LOGGER
+from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
 from utils.plots import feature_visualization
-from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
-    select_device, time_sync
+from utils.torch_utils import (copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device,
+                               time_sync)
 
 try:
     import thop  # for FLOPs computation

setup.cfg

     F403
     E302
     F541
+
+
+[isort]
+# https://pycqa.github.io/isort/docs/configuration/options.html
+line_length = 120
+multi_line_output = 0

train.py

 import yaml
 from torch.cuda import amp
 from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.optim import Adam, SGD, lr_scheduler
+from torch.optim import SGD, Adam, lr_scheduler
 from tqdm import tqdm
 
 FILE = Path(__file__).resolve()
 from models.yolo import Model
 from utils.autoanchor import check_anchors
 from utils.autobatch import check_train_batch_size
+from utils.callbacks import Callbacks
 from utils.datasets import create_dataloader
-from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
-    strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
-    check_file, check_yaml, check_suffix, print_args, print_mutation, one_cycle, colorstr, methods, LOGGER
 from utils.downloads import attempt_download
-from utils.loss import ComputeLoss
-from utils.plots import plot_labels, plot_evolve
-from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, intersect_dicts, select_device, \
-    torch_distributed_zero_first
+from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,
+                           check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,
+                           labels_to_class_weights, labels_to_image_weights, methods, one_cycle, print_args,
+                           print_mutation, strip_optimizer)
+from utils.loggers import Loggers
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
+from utils.loss import ComputeLoss
 from utils.metrics import fitness
-from utils.loggers import Loggers
-from utils.callbacks import Callbacks
+from utils.plots import plot_evolve, plot_labels
+from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, intersect_dicts, select_device,
+                               torch_distributed_zero_first)
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))

utils/augmentations.py

 import cv2
 import numpy as np
 
-from utils.general import colorstr, segment2box, resample_segments, check_version
+from utils.general import check_version, colorstr, resample_segments, segment2box
 from utils.metrics import bbox_ioa
 
 

utils/datasets.py

 import shutil
 import time
 from itertools import repeat
-from multiprocessing.pool import ThreadPool, Pool
+from multiprocessing.pool import Pool, ThreadPool
 from pathlib import Path
 from threading import Thread
 from zipfile import ZipFile
 import torch
 import torch.nn.functional as F
 import yaml
-from PIL import Image, ImageOps, ExifTags
+from PIL import ExifTags, Image, ImageOps
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
 from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective
-from utils.general import check_dataset, check_requirements, check_yaml, clean_str, segments2boxes, \
-    xywh2xyxy, xywhn2xyxy, xyxy2xywhn, xyn2xy, LOGGER
+from utils.general import (LOGGER, check_dataset, check_requirements, check_yaml, clean_str, segments2boxes, xyn2xy,
+                           xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters

utils/flask_rest_api/restapi.py

 import io
 
 import torch
-from PIL import Image
 from flask import Flask, request
+from PIL import Image
 
 app = Flask(__name__)
 

utils/loggers/wandb/sweep.py

 if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 
-from train import train, parse_opt
+from train import parse_opt, train
+from utils.callbacks import Callbacks
 from utils.general import increment_path
 from utils.torch_utils import select_device
-from utils.callbacks import Callbacks
 
 
 def sweep():

utils/loggers/wandb/wandb_utils.py

 if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 
-from utils.datasets import LoadImagesAndLabels
-from utils.datasets import img2label_paths
+from utils.datasets import LoadImagesAndLabels, img2label_paths
 from utils.general import check_dataset, check_file
 
 try:

utils/plots.py

 import torch
 from PIL import Image, ImageDraw, ImageFont
 
-from utils.general import user_config_dir, is_ascii, is_chinese, xywh2xyxy, xyxy2xywh
+from utils.general import is_ascii, is_chinese, user_config_dir, xywh2xyxy, xyxy2xywh
 from utils.metrics import fitness
 
 # Settings

val.py

 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.experimental import attempt_load
+from utils.callbacks import Callbacks
 from utils.datasets import create_dataloader
-from utils.general import box_iou, coco80_to_coco91_class, colorstr, check_dataset, check_img_size, \
-    check_requirements, check_suffix, check_yaml, increment_path, non_max_suppression, print_args, scale_coords, \
-    xyxy2xywh, xywh2xyxy, LOGGER
-from utils.metrics import ap_per_class, ConfusionMatrix
+from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_suffix, check_yaml,
+                           coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
+                           scale_coords, xywh2xyxy, xyxy2xywh)
+from utils.metrics import ConfusionMatrix, ap_per_class
 from utils.plots import output_to_target, plot_images, plot_val_study
 from utils.torch_utils import select_device, time_sync
-from utils.callbacks import Callbacks
 
 
 def save_one_txt(predn, save_conf, shape, file):