reformat with isort (fcakyon#71)

yolov5/cli.py

@@ -1,9 +1,9 @@
 import fire
 
-from yolov5.train import run as train
-from yolov5.val import run as val
 from yolov5.detect import run as detect
 from yolov5.export import run as export
+from yolov5.train import run as train
+from yolov5.val import run as val
 
 
 def app() -> None:

yolov5/detect.py

@@ -21,9 +21,13 @@
 
 from yolov5.models.experimental import attempt_load
 from yolov5.utils.datasets import LoadImages, LoadStreams
-from yolov5.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, set_logging, \
-    strip_optimizer, xyxy2xywh, yolov5_in_syspath
+from yolov5.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, set_logging,
+                                  strip_optimizer, xyxy2xywh,
+                                  yolov5_in_syspath)
 from yolov5.utils.plots import Annotator, colors
 from yolov5.utils.torch_utils import load_classifier, select_device, time_sync
 

yolov5/export.py

@@ -39,8 +39,9 @@
 from yolov5.models.yolo import Detect
 from yolov5.utils.activations import SiLU
 from yolov5.utils.datasets import LoadImages
-from yolov5.utils.general import colorstr, check_dataset, check_img_size, check_requirements, file_size, print_args, \
-    set_logging, url2file
+from yolov5.utils.general import (check_dataset, check_img_size,
+                                  check_requirements, colorstr, file_size,
+                                  print_args, set_logging, url2file)
 from yolov5.utils.torch_utils import select_device
 
 
@@ -131,8 +132,8 @@ def export_saved_model(model, im, file, dynamic,
     keras_model = None
     try:
         import tensorflow as tf
+        from models.tf import TFDetect, TFModel
         from tensorflow import keras
-        from models.tf import TFModel, TFDetect
 
         print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         f = str(file).replace('.pt', '_saved_model')
@@ -159,7 +160,8 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
     # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
     try:
         import tensorflow as tf
-        from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+        from tensorflow.python.framework.convert_to_constants import \
+            convert_variables_to_constants_v2
 
         print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         f = file.with_suffix('.pb')
@@ -212,6 +214,7 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
     try:
         #check_requirements(('tensorflowjs',))
         import re
+
         import tensorflowjs as tfjs
 
         print(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')

yolov5/helpers.py

@@ -1,11 +1,10 @@
 from pathlib import Path
 
-from yolov5.models.yolo import Model
 from yolov5.models.experimental import attempt_load
+from yolov5.models.yolo import Model
 from yolov5.utils.general import set_logging, yolov5_in_syspath
 from yolov5.utils.google_utils import attempt_download
-from yolov5.utils.torch_utils import select_device
-from yolov5.utils.torch_utils import torch
+from yolov5.utils.torch_utils import select_device, torch
 
 
 def load_model(model_path, device=None, autoshape=True, verbose=False):

yolov5/hubconf.py

@@ -27,10 +27,11 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     """
     from pathlib import Path
 
-    from yolov5.models.yolo import Model
     from yolov5.models.experimental import attempt_load
-    from yolov5.utils.general import check_requirements, set_logging, yolov5_in_syspath
+    from yolov5.models.yolo import Model
     from yolov5.utils.downloads import attempt_download
+    from yolov5.utils.general import (check_requirements, set_logging,
+                                      yolov5_in_syspath)
     from yolov5.utils.torch_utils import select_device
 
     file = Path(__file__).resolve()
@@ -126,10 +127,11 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=Tr
     # 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

yolov5/models/common.py

@@ -18,8 +18,9 @@
 from torch.cuda import amp
 
 from yolov5.utils.datasets import exif_transpose, letterbox
-from yolov5.utils.general import colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, \
-    scale_coords, xyxy2xywh
+from yolov5.utils.general import (colorstr, increment_path, make_divisible,
+                                  non_max_suppression, save_one_box,
+                                  scale_coords, xyxy2xywh)
 from yolov5.utils.plots import Annotator, colors
 from yolov5.utils.torch_utils import time_sync
 

yolov5/models/experimental.py

@@ -8,8 +8,8 @@
 import torch.nn as nn
 
 from yolov5.models.common import Conv
-from yolov5.utils.general import yolov5_in_syspath
 from yolov5.utils.downloads import attempt_download
+from yolov5.utils.general import yolov5_in_syspath
 
 
 class CrossConv(nn.Module):

yolov5/models/tf.py

@@ -25,11 +25,12 @@
 import torch.nn as nn
 from tensorflow import keras
 
-from yolov5.models.common import Bottleneck, BottleneckCSP, Concat, Conv, C3, DWConv, Focus, SPP, SPPF, autopad
+from yolov5.models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP,
+                                  Concat, Conv, DWConv, Focus, autopad)
 from yolov5.models.experimental import CrossConv, MixConv2d, attempt_load
 from yolov5.models.yolo import Detect
-from yolov5.utils.general import make_divisible, print_args, set_logging
 from yolov5.utils.activations import SiLU
+from yolov5.utils.general import make_divisible, print_args, set_logging
 
 LOGGER = logging.getLogger(__name__)
 

yolov5/models/yolo.py

@@ -17,10 +17,12 @@
 from yolov5.models.common import *
 from yolov5.models.experimental import *
 from yolov5.utils.autoanchor import check_anchor_order
-from yolov5.utils.general import check_yaml, make_divisible, print_args, set_logging
+from yolov5.utils.general import (check_yaml, make_divisible, print_args,
+                                  set_logging)
 from yolov5.utils.plots import feature_visualization
-from yolov5.utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
-    select_device, time_sync
+from yolov5.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

yolov5/train.py

@@ -24,7 +24,7 @@
 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()
@@ -34,20 +34,26 @@
 from yolov5.models.experimental import attempt_load
 from yolov5.models.yolo import Model
 from yolov5.utils.autoanchor import check_anchors
+from yolov5.utils.aws import upload_file_to_s3, upload_folder_to_s3
+from yolov5.utils.callbacks import Callbacks
 from yolov5.utils.datasets import create_dataloader
-from yolov5.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, set_logging, one_cycle, colorstr, methods, yolov5_in_syspath
 from yolov5.utils.downloads import attempt_download
-from yolov5.utils.loss import ComputeLoss
-from yolov5.utils.plots import plot_labels, plot_evolve
-from yolov5.utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, intersect_dicts, select_device, \
-    torch_distributed_zero_first
+from yolov5.utils.general import (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, set_logging,
+                                  strip_optimizer, yolov5_in_syspath)
+from yolov5.utils.loggers import Loggers
 from yolov5.utils.loggers.wandb.wandb_utils import check_wandb_resume
+from yolov5.utils.loss import ComputeLoss
 from yolov5.utils.metrics import fitness
-from yolov5.utils.loggers import Loggers
-from yolov5.utils.callbacks import Callbacks
-from yolov5.utils.aws import upload_file_to_s3, upload_folder_to_s3
+from yolov5.utils.plots import plot_evolve, plot_labels
+from yolov5.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel,
+                                      intersect_dicts, select_device,
+                                      torch_distributed_zero_first)
 
 LOGGER = logging.getLogger(__name__)
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html

yolov5/utils/augmentations.py

@@ -10,7 +10,8 @@
 import cv2
 import numpy as np
 
-from yolov5.utils.general import colorstr, segment2box, resample_segments, check_version
+from yolov5.utils.general import (check_version, colorstr, resample_segments,
+                                  segment2box)
 from yolov5.utils.metrics import bbox_ioa
 
 

yolov5/utils/aws.py

@@ -1,13 +1,14 @@
-import os
 import concurrent.futures
-from pathlib import Path
 import logging
+import os
+from pathlib import Path
+
 import boto3
+import numpy as np
 from botocore.exceptions import NoCredentialsError
 from tqdm import tqdm
-import numpy as np
-from yolov5.utils.general import colorstr
 
+from yolov5.utils.general import colorstr
 
 AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
 AWS_SECRET_ACCESS_KEY = os.environ.get("AWS_SECRET_ACCESS_KEY")

yolov5/utils/datasets.py

@@ -12,7 +12,7 @@
 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
@@ -22,13 +22,17 @@
 import torch
 import torch.nn.functional as F
 import yaml
-from PIL import Image, ExifTags
+from PIL import ExifTags, Image
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from yolov5.utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective
-from yolov5.utils.general import check_dataset, check_requirements, check_yaml, clean_str, segments2boxes, \
-    xywh2xyxy, xywhn2xyxy, xyxy2xywhn, xyn2xy, yolov5_in_syspath
+from yolov5.utils.augmentations import (Albumentations, augment_hsv,
+                                        copy_paste, letterbox, mixup,
+                                        random_perspective)
+from yolov5.utils.general import (check_dataset, check_requirements,
+                                  check_yaml, clean_str, segments2boxes,
+                                  xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn,
+                                  yolov5_in_syspath)
 from yolov5.utils.torch_utils import torch_distributed_zero_first
 
 # Parameters

yolov5/utils/general.py

@@ -12,6 +12,7 @@
 import random
 import re
 import signal
+import sys
 import time
 import urllib
 from itertools import repeat
@@ -27,7 +28,6 @@
 import torch
 import torchvision
 import yaml
-import sys
 
 from yolov5.utils.downloads import gsutil_getsize
 from yolov5.utils.metrics import box_iou, fitness

yolov5/utils/loggers/__init__.py

@@ -4,17 +4,17 @@
 """
 
 import os
-from pathlib import Path
 import warnings
+from pathlib import Path
 from threading import Thread
 
 import pkg_resources as pkg
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
 from yolov5.utils.general import colorstr, emojis
-from yolov5.utils.loggers.wandb.wandb_utils import WandbLogger
 from yolov5.utils.loggers.neptune.neptune_utils import NeptuneLogger
+from yolov5.utils.loggers.wandb.wandb_utils import WandbLogger
 from yolov5.utils.plots import plot_images, plot_results
 from yolov5.utils.torch_utils import de_parallel
 

yolov5/utils/loggers/neptune/neptune_utils.py

@@ -1,8 +1,9 @@
+import os
 from pathlib import Path
 
-from yolov5.utils.general import colorstr
 import yaml
-import os
+
+from yolov5.utils.general import colorstr
 
 try:
     import neptune.new as neptune

yolov5/utils/loggers/wandb/sweep.py

@@ -6,10 +6,10 @@
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[3]  # YOLOv5 root directory
 
-from yolov5.train import train, parse_opt
+from yolov5.train import parse_opt, train
+from yolov5.utils.callbacks import Callbacks
 from yolov5.utils.general import increment_path
 from yolov5.utils.torch_utils import select_device
-from yolov5.utils.callbacks import Callbacks
 
 
 def sweep():

yolov5/utils/loggers/wandb/wandb_utils.py

@@ -13,8 +13,7 @@
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[3]  # YOLOv5 root directory
 
-from yolov5.utils.datasets import LoadImagesAndLabels
-from yolov5.utils.datasets import img2label_paths
+from yolov5.utils.datasets import LoadImagesAndLabels, img2label_paths
 from yolov5.utils.general import check_dataset, check_file
 
 RANK = int(os.getenv('RANK', -1))

yolov5/utils/plots.py

@@ -17,7 +17,8 @@
 import torch
 from PIL import Image, ImageDraw, ImageFont
 
-from yolov5.utils.general import user_config_dir, is_ascii, is_chinese, xywh2xyxy, xyxy2xywh
+from yolov5.utils.general import (is_ascii, is_chinese, user_config_dir,
+                                  xywh2xyxy, xyxy2xywh)
 from yolov5.utils.metrics import fitness
 
 # Settings

yolov5/val.py

@@ -14,22 +14,25 @@
 from threading import Thread
 
 import numpy as np
+import pandas as pd
 import torch
 from tqdm import tqdm
-import pandas as pd
 
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[0]  # YOLOv5 root directory
 
 from yolov5.models.experimental import attempt_load
+from yolov5.utils.callbacks import Callbacks
 from yolov5.utils.datasets import create_dataloader
-from yolov5.utils.general import coco80_to_coco91_class, check_dataset, check_img_size, check_requirements, \
-    check_suffix, check_yaml, box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, \
-    increment_path, colorstr, print_args
-from yolov5.utils.metrics import ap_per_class, ConfusionMatrix
+from yolov5.utils.general import (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, set_logging,
+                                  xywh2xyxy, xyxy2xywh)
+from yolov5.utils.metrics import ConfusionMatrix, ap_per_class
 from yolov5.utils.plots import output_to_target, plot_images, plot_val_study
 from yolov5.utils.torch_utils import select_device, time_sync
-from yolov5.utils.callbacks import Callbacks
 
 
 def save_one_txt(predn, save_conf, shape, file):