New global `TQDM_BAR_FORMAT` (#10211)

classify/train.py

@@ -40,8 +40,8 @@ from classify import val as validate
 from models.experimental import attempt_load
 from models.yolo import ClassificationModel, DetectionModel
 from utils.dataloaders import create_classification_dataloader
-from utils.general import (DATASETS_DIR, LOGGER, WorkingDirectory, check_git_status, check_requirements, colorstr,
-                           download, increment_path, init_seeds, print_args, yaml_save)
+from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_status,
+                           check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save)
 from utils.loggers import GenericLogger
 from utils.plots import imshow_cls
 from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
@@ -174,7 +174,7 @@ def train(opt, device):
             trainloader.sampler.set_epoch(epoch)
         pbar = enumerate(trainloader)
         if RANK in {-1, 0}:
-            pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
+            pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)
         for i, (images, labels) in pbar:  # progress bar
             images, labels = images.to(device, non_blocking=True), labels.to(device)
 

classify/val.py

@@ -36,7 +36,8 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.common import DetectMultiBackend
 from utils.dataloaders import create_classification_dataloader
-from utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
+from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr,
+                           increment_path, print_args)
 from utils.torch_utils import select_device, smart_inference_mode
 
 
@@ -100,7 +101,7 @@ def run(
     n = len(dataloader)  # number of batches
     action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
     desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
-    bar = tqdm(dataloader, desc, n, not training, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}', position=0)
+    bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
     with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
         for images, labels in bar:
             with dt[0]:

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')

segment/val.py

@@ -42,9 +42,9 @@ import torch.nn.functional as F
 from models.common import DetectMultiBackend
 from models.yolo import SegmentationModel
 from utils.callbacks import Callbacks
-from utils.general import (LOGGER, NUM_THREADS, Profile, check_dataset, check_img_size, check_requirements, check_yaml,
-                           coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
-                           scale_boxes, xywh2xyxy, xyxy2xywh)
+from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size,
+                           check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path,
+                           non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, box_iou
 from utils.plots import output_to_target, plot_val_study
 from utils.segment.dataloaders import create_dataloader
@@ -237,7 +237,7 @@ def run(
     loss = torch.zeros(4, device=device)
     jdict, stats = [], []
     # callbacks.run('on_val_start')
-    pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
+    pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT)  # progress bar
     for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
         # callbacks.run('on_val_batch_start')
         with dt[0]:

train.py

@@ -47,10 +47,11 @@ from utils.autobatch import check_train_batch_size
 from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
 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, methods,
-                           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, methods, one_cycle, print_args, print_mutation, strip_optimizer,
+                           yaml_save)
 from utils.loggers import Loggers
 from utils.loggers.comet.comet_utils import check_comet_resume
 from utils.loss import ComputeLoss
@@ -275,7 +276,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
         pbar = enumerate(train_loader)
         LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_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, _) in pbar:  # batch -------------------------------------------------------------
             callbacks.run('on_train_batch_start')

utils/autoanchor.py

@@ -11,7 +11,7 @@ import yaml
 from tqdm import tqdm
 
 from utils import TryExcept
-from utils.general import LOGGER, colorstr
+from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
 
 PREFIX = colorstr('AutoAnchor: ')
 
@@ -153,7 +153,7 @@ 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), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
+    pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT)  # progress bar
     for _ in pbar:
         v = np.ones(sh)
         while (v == 1).all():  # mutate until a change occurs (prevent duplicates)

utils/dataloaders.py

@@ -29,17 +29,16 @@ from torch.utils.data import DataLoader, Dataset, dataloader, distributed
 from tqdm import tqdm
 
 from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
-                                 cutout, letterbox, mixup, random_perspective)
-from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str,
-                           cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, xywh2xyxy, xywhn2xyxy,
-                           xyxy2xywhn)
+                                 letterbox, mixup, random_perspective)
+from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements,
+                           check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy,
+                           xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
 HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # 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
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))
 PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
@@ -494,7 +493,7 @@ class LoadImagesAndLabels(Dataset):
         nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total
         if exists and LOCAL_RANK in {-1, 0}:
             d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
-            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT)  # display cache results
+            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_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 found in {cache_path}, can not start training. {HELP_URL}'
@@ -576,7 +575,7 @@ 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, bar_format=BAR_FORMAT, disable=LOCAL_RANK > 0)
+            pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
             for i, x in pbar:
                 if cache_images == 'disk':
                     b += self.npy_files[i].stat().st_size
@@ -612,7 +611,7 @@ class LoadImagesAndLabels(Dataset):
             pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
                         desc=desc,
                         total=len(self.im_files),
-                        bar_format=BAR_FORMAT)
+                        bar_format=TQDM_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

utils/general.py

@@ -50,6 +50,7 @@ NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiproces
 DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets'))  # global datasets directory
 AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode
 VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true'  # global verbose mode
+TQDM_BAR_FORMAT = '{l_bar}{bar:10}| {n_fmt}/{total_fmt} {elapsed}'  # tqdm bar format
 FONT = 'Arial.ttf'  # https://ultralytics.com/assets/Arial.ttf
 
 torch.set_printoptions(linewidth=320, precision=5, profile='long')

val.py

@@ -38,9 +38,9 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.common import DetectMultiBackend
 from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
-from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_yaml,
-                           coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
-                           scale_boxes, xywh2xyxy, xyxy2xywh)
+from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements,
+                           check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression,
+                           print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
 from utils.plots import output_to_target, plot_images, plot_val_study
 from utils.torch_utils import select_device, smart_inference_mode
@@ -193,7 +193,7 @@ def run(
     loss = torch.zeros(3, device=device)
     jdict, stats, ap, ap_class = [], [], [], []
     callbacks.run('on_val_start')
-    pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
+    pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT)  # progress bar
     for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
         callbacks.run('on_val_batch_start')
         with dt[0]: