Update train, val tqdm to fixed width

train.py

@@ -289,7 +289,7 @@ 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, 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)

val.py

@@ -160,7 +160,8 @@ 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, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
+    pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
+    for batch_i, (img, targets, paths, shapes) in enumerate(pbar):
         t1 = time_sync()
         img = img.to(device, non_blocking=True)
         img = img.half() if half else img.float()  # uint8 to fp16/32