New global TQDM_BAR_FORMAT (#10211)

* New global TQDM_BAR_FORMAT

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

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

segment/train.py

@@ -46,10 +46,10 @@ from utils.autoanchor import check_anchors
 from utils.autobatch import check_train_batch_size
 from utils.callbacks import Callbacks
 from utils.downloads import attempt_download, is_url
-from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_git_status, check_img_size,
-                           check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
-                           init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, one_cycle,
-                           print_args, print_mutation, strip_optimizer, yaml_save)
+from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_status,
+                           check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run,
+                           increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
+                           labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
 from utils.loggers import GenericLogger
 from utils.plots import plot_evolve, plot_labels
 from utils.segment.dataloaders import create_dataloader
@@ -277,7 +277,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
         LOGGER.info(('\n' + '%11s' * 8) %
                     ('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
         if RANK in {-1, 0}:
-            pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
+            pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT)  # progress bar
         optimizer.zero_grad()
         for i, (imgs, targets, paths, _, masks) in pbar:  # batch ------------------------------------------------------
             # callbacks.run('on_train_batch_start')