Update train, val tqdm to fixed width (#5367)

* Update tqdm for fixed width

* Update val.py

* Update val.py

* Try ncols= in train.py

* NCOLS

* NCOLS

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

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

* bar_format

* position 0 leave true

* exp0

* auto

* auto

* Cleanup

* Cleanup

* Cleanup

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

train.py

@@ -5,7 +5,6 @@ Train a YOLOv5 model on a custom dataset
 Usage:
     $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
 """
-
 import argparse
 import math
 import os
@@ -40,10 +39,10 @@ from utils.autobatch import check_train_batch_size
 from utils.callbacks import Callbacks
 from utils.datasets import create_dataloader
 from utils.downloads import attempt_download
-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,
-                           intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle,
-                           print_args, print_mutation, strip_optimizer)
+from utils.general import (LOGGER, NCOLS, 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, 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
@@ -289,7 +288,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
         pbar = enumerate(train_loader)
         LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
         if RANK in [-1, 0]:
-            pbar = tqdm(pbar, total=nb)  # progress bar
+            pbar = tqdm(pbar, total=nb, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
         optimizer.zero_grad()
         for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
             ni = i + nb * epoch  # number integrated batches (since train start)