Update setup.cfg (#11814)

* Update setup.cfg

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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

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

* Update .pre-commit-config.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update export.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update downloads.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update yolo.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

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

utils/augmentations.py

@@ -330,7 +330,7 @@ def classify_albumentations(
                 if vflip > 0:
                     T += [A.VerticalFlip(p=vflip)]
                 if jitter > 0:
-                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
+                    color_jitter = (float(jitter), ) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
                     T += [A.ColorJitter(*color_jitter, 0)]
         else:  # Use fixed crop for eval set (reproducibility)
             T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]

utils/callbacks.py

@@ -32,7 +32,7 @@ class Callbacks:
             'on_model_save': [],
             'on_train_end': [],
             'on_params_update': [],
-            'teardown': [],}
+            'teardown': [], }
         self.stop_training = False  # set True to interrupt training
 
     def register_action(self, hook, name='', callback=None):

utils/downloads.py

@@ -4,7 +4,6 @@ Download utils
 """
 
 import logging
-import os
 import subprocess
 import urllib
 from pathlib import Path
@@ -53,7 +52,7 @@ def curl_download(url, filename, *, silent: bool = False) -> bool:
         '--retry',
         '9',
         '-C',
-        '-',])
+        '-', ])
     return proc.returncode == 0
 
 

utils/general.py

@@ -139,12 +139,12 @@ def set_logging(name=LOGGING_NAME, verbose=True):
             name: {
                 'class': 'logging.StreamHandler',
                 'formatter': name,
-                'level': level,}},
+                'level': level, }},
         'loggers': {
             name: {
                 'level': level,
                 'handlers': [name],
-                'propagate': False,}}})
+                'propagate': False, }}})
 
 
 set_logging(LOGGING_NAME)  # run before defining LOGGER
@@ -416,7 +416,7 @@ def check_imshow(warn=False):
         return False
 
 
-def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
+def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''):
     # Check file(s) for acceptable suffix
     if file and suffix:
         if isinstance(suffix, str):

utils/loggers/comet/__init__.py

@@ -90,7 +90,7 @@ class CometLogger:
             'log_code': False,
             'log_env_gpu': True,
             'log_env_cpu': True,
-            'project_name': COMET_PROJECT_NAME,}
+            'project_name': COMET_PROJECT_NAME, }
         self.default_experiment_kwargs.update(experiment_kwargs)
         self.experiment = self._get_experiment(self.comet_mode, run_id)
         self.experiment.set_name(self.opt.name)
@@ -153,7 +153,7 @@ class CometLogger:
             'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS,
             'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS,
             'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX,
-            'comet_model_name': COMET_MODEL_NAME,})
+            'comet_model_name': COMET_MODEL_NAME, })
 
         # Check if running the Experiment with the Comet Optimizer
         if hasattr(self.opt, 'comet_optimizer_id'):
@@ -170,7 +170,7 @@ class CometLogger:
                     **self.default_experiment_kwargs,
                 )
 
-            return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,)
+            return comet_ml.OfflineExperiment(**self.default_experiment_kwargs, )
 
         else:
             try:
@@ -214,7 +214,7 @@ class CometLogger:
             'fitness_score': fitness_score[-1],
             'epochs_trained': epoch + 1,
             'save_period': opt.save_period,
-            'total_epochs': opt.epochs,}
+            'total_epochs': opt.epochs, }
 
         model_files = glob.glob(f'{path}/*.pt')
         for model_path in model_files:
@@ -270,7 +270,7 @@ class CometLogger:
                     'x': xyxy[0],
                     'y': xyxy[1],
                     'x2': xyxy[2],
-                    'y2': xyxy[3]},})
+                    'y2': xyxy[3]}, })
         for *xyxy, conf, cls in filtered_detections.tolist():
             metadata.append({
                 'label': f'{self.class_names[int(cls)]}',
@@ -279,7 +279,7 @@ class CometLogger:
                     'x': xyxy[0],
                     'y': xyxy[1],
                     'x2': xyxy[2],
-                    'y2': xyxy[3]},})
+                    'y2': xyxy[3]}, })
 
         self.metadata_dict[image_name] = metadata
         self.logged_images_count += 1

utils/segment/metrics.py

@@ -196,7 +196,7 @@ KEYS = [
     'val/cls_loss',
     'x/lr0',
     'x/lr1',
-    'x/lr2',]
+    'x/lr2', ]
 
 BEST_KEYS = [
     'best/epoch',
@@ -207,4 +207,4 @@ BEST_KEYS = [
     'best/precision(M)',
     'best/recall(M)',
     'best/mAP_0.5(M)',
-    'best/mAP_0.5:0.95(M)',]
+    'best/mAP_0.5:0.95(M)', ]

utils/torch_utils.py

@@ -170,7 +170,7 @@ def profile(input, ops, n=10, device=None):
             m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
             tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
             try:
-                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
+                flops = thop.profile(m, inputs=(x, ), verbose=False)[0] / 1E9 * 2  # GFLOPs
             except Exception:
                 flops = 0
 
@@ -284,7 +284,7 @@ def model_info(model, verbose=False, imgsz=640):
         p = next(model.parameters())
         stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
         im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
-        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
+        flops = thop.profile(deepcopy(model), inputs=(im, ), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
         imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
         fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs'  # 640x640 GFLOPs
     except Exception: