Update TQDM bar format (#6988)

utils/autoanchor.py

@@ -152,7 +152,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
 
     # Evolve
     f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc=f'{PREFIX}Evolving anchors with Genetic Algorithm:')  # progress bar
+    pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
     for _ in pbar:
         v = np.ones(sh)
         while (v == 1).all():  # mutate until a change occurs (prevent duplicates)

utils/datasets.py

@@ -35,6 +35,7 @@ from utils.torch_utils import torch_distributed_zero_first
 HELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp'  # include image suffixes
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
+BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}'  # tqdm bar format
 
 # Get orientation exif tag
 for orientation in ExifTags.TAGS.keys():
@@ -427,7 +428,7 @@ class LoadImagesAndLabels(Dataset):
         nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total
         if exists:
             d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupt"
-            tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
+            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT)  # display cache results
             if cache['msgs']:
                 LOGGER.info('\n'.join(cache['msgs']))  # display warnings
         assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {HELP_URL}'
@@ -492,7 +493,7 @@ class LoadImagesAndLabels(Dataset):
             self.im_hw0, self.im_hw = [None] * n, [None] * n
             fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
             results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
-            pbar = tqdm(enumerate(results), total=n)
+            pbar = tqdm(enumerate(results), total=n, bar_format=BAR_FORMAT)
             for i, x in pbar:
                 if cache_images == 'disk':
                     gb += self.npy_files[i].stat().st_size
@@ -509,7 +510,7 @@ class LoadImagesAndLabels(Dataset):
         desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
         with Pool(NUM_THREADS) as pool:
             pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
-                        desc=desc, total=len(self.im_files))
+                        desc=desc, total=len(self.im_files), bar_format=BAR_FORMAT)
             for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
                 nm += nm_f
                 nf += nf_f