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

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)

utils/general.py

@@ -11,6 +11,7 @@ import os
 import platform
 import random
 import re
+import shutil
 import signal
 import time
 import urllib
@@ -834,3 +835,7 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
     if mkdir:
         path.mkdir(parents=True, exist_ok=True)  # make directory
     return path
+
+
+# Variables
+NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns  # terminal window size

val.py

@@ -26,7 +26,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.common import DetectMultiBackend
 from utils.callbacks import Callbacks
 from utils.datasets import create_dataloader
-from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,
+from utils.general import (LOGGER, NCOLS, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,
                            coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
                            scale_coords, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, ap_per_class
@@ -162,7 +162,8 @@ def run(data,
     dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
     loss = torch.zeros(3, device=device)
     jdict, stats, ap, ap_class = [], [], [], []
-    for batch_i, (im, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
+    pbar = tqdm(dataloader, desc=s, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
+    for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
         t1 = time_sync()
         if pt:
             im = im.to(device, non_blocking=True)