Update get_loggers() (#4854)

* Update `set_logging()`

* Update export.py

* pre-commit fixes

* Update LoadImages

* Update LoadStreams

* Update print_args

* Single LOGGER definition

* yolo.py fix

Co-authored-by: pre-commit <pre-commit@example.com>

train.py

@@ -40,7 +40,7 @@ from utils.autobatch import check_train_batch_size
 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, set_logging, one_cycle, colorstr, methods
+    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
@@ -51,7 +51,6 @@ from utils.metrics import fitness
 from utils.loggers import Loggers
 from utils.callbacks import Callbacks
 
-LOGGER = logging.getLogger(__name__)
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))
 WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
@@ -129,7 +128,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     for k, v in model.named_parameters():
         v.requires_grad = True  # train all layers
         if any(x in k for x in freeze):
-            print(f'freezing {k}')
+            LOGGER.info(f'freezing {k}')
             v.requires_grad = False
 
     # Image size
@@ -485,7 +484,6 @@ def parse_opt(known=False):
 
 def main(opt, callbacks=Callbacks()):
     # Checks
-    set_logging(RANK)
     if RANK in [-1, 0]:
         print_args(FILE.stem, opt)
         check_git_status()
@@ -609,9 +607,9 @@ def main(opt, callbacks=Callbacks()):
 
         # Plot results
         plot_evolve(evolve_csv)
-        print(f'Hyperparameter evolution finished\n'
-              f"Results saved to {colorstr('bold', save_dir)}\n"
-              f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
+        LOGGER.info(f'Hyperparameter evolution finished\n'
+                    f"Results saved to {colorstr('bold', save_dir)}\n"
+                    f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
 
 
 def run(**kwargs):