New global `TQDM_BAR_FORMAT` (#10211)
* New global TQDM_BAR_FORMAT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/10039/head
Glenn Jocher
GitHub
parent
74b3886edd
commit
0322bb3196
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@ -40,8 +40,8 @@ from classify import val as validate
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from models.experimental import attempt_load
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from models.yolo import ClassificationModel, DetectionModel
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from utils.dataloaders import create_classification_dataloader
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from utils.general import (DATASETS_DIR, LOGGER, WorkingDirectory, check_git_status, check_requirements, colorstr,
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download, increment_path, init_seeds, print_args, yaml_save)
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from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_status,
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check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save)
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from utils.loggers import GenericLogger
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from utils.plots import imshow_cls
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from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
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@ -174,7 +174,7 @@ def train(opt, device):
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trainloader.sampler.set_epoch(epoch)
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pbar = enumerate(trainloader)
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if RANK in {-1, 0}:
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pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
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pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)
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for i, (images, labels) in pbar: # progress bar
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images, labels = images.to(device, non_blocking=True), labels.to(device)
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@ -36,7 +36,8 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import DetectMultiBackend
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from utils.dataloaders import create_classification_dataloader
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from utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
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from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr,
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increment_path, print_args)
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from utils.torch_utils import select_device, smart_inference_mode
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@ -100,7 +101,7 @@ def run(
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n = len(dataloader) # number of batches
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action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
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desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
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bar = tqdm(dataloader, desc, n, not training, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}', position=0)
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bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
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with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
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for images, labels in bar:
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with dt[0]:
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@ -46,10 +46,10 @@ from utils.autoanchor import check_anchors
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from utils.autobatch import check_train_batch_size
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from utils.callbacks import Callbacks
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from utils.downloads import attempt_download, is_url
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from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_git_status, check_img_size,
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check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
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init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, one_cycle,
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print_args, print_mutation, strip_optimizer, yaml_save)
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from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_status,
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check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run,
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increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
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labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
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from utils.loggers import GenericLogger
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from utils.plots import plot_evolve, plot_labels
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from utils.segment.dataloaders import create_dataloader
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@ -277,7 +277,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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LOGGER.info(('\n' + '%11s' * 8) %
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('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
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if RANK in {-1, 0}:
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pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
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pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
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optimizer.zero_grad()
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for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------
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# callbacks.run('on_train_batch_start')
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@ -42,9 +42,9 @@ import torch.nn.functional as F
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from models.common import DetectMultiBackend
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from models.yolo import SegmentationModel
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from utils.callbacks import Callbacks
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from utils.general import (LOGGER, NUM_THREADS, Profile, check_dataset, check_img_size, check_requirements, check_yaml,
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coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
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scale_boxes, xywh2xyxy, xyxy2xywh)
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from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size,
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check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path,
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non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
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from utils.metrics import ConfusionMatrix, box_iou
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from utils.plots import output_to_target, plot_val_study
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from utils.segment.dataloaders import create_dataloader
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@ -237,7 +237,7 @@ def run(
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loss = torch.zeros(4, device=device)
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jdict, stats = [], []
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# callbacks.run('on_val_start')
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pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
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pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
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for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
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# callbacks.run('on_val_batch_start')
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with dt[0]:
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11
train.py
11
train.py
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@ -47,10 +47,11 @@ from utils.autobatch import check_train_batch_size
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from utils.callbacks import Callbacks
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from utils.dataloaders import create_dataloader
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from utils.downloads import attempt_download, is_url
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from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_git_status, check_img_size,
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check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
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init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods,
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one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
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from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_status,
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check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run,
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increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
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labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer,
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yaml_save)
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from utils.loggers import Loggers
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from utils.loggers.comet.comet_utils import check_comet_resume
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from utils.loss import ComputeLoss
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@ -275,7 +276,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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pbar = enumerate(train_loader)
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LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
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if RANK in {-1, 0}:
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pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
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pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
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optimizer.zero_grad()
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for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
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callbacks.run('on_train_batch_start')
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@ -11,7 +11,7 @@ import yaml
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from tqdm import tqdm
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from utils import TryExcept
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from utils.general import LOGGER, colorstr
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from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
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PREFIX = colorstr('AutoAnchor: ')
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@ -153,7 +153,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
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# Evolve
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f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
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pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
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pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar
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for _ in pbar:
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v = np.ones(sh)
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while (v == 1).all(): # mutate until a change occurs (prevent duplicates)
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@ -29,17 +29,16 @@ from torch.utils.data import DataLoader, Dataset, dataloader, distributed
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from tqdm import tqdm
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from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
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cutout, letterbox, mixup, random_perspective)
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from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str,
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cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, xywh2xyxy, xywhn2xyxy,
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xyxy2xywhn)
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letterbox, mixup, random_perspective)
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from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements,
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check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy,
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xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
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from utils.torch_utils import torch_distributed_zero_first
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# Parameters
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HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
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IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes
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VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
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BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}' # tqdm bar format
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
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RANK = int(os.getenv('RANK', -1))
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PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders
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@ -494,7 +493,7 @@ class LoadImagesAndLabels(Dataset):
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nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total
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if exists and LOCAL_RANK in {-1, 0}:
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d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
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tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT) # display cache results
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tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results
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if cache['msgs']:
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LOGGER.info('\n'.join(cache['msgs'])) # display warnings
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assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}'
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@ -576,7 +575,7 @@ class LoadImagesAndLabels(Dataset):
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self.im_hw0, self.im_hw = [None] * n, [None] * n
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fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
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results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
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pbar = tqdm(enumerate(results), total=n, bar_format=BAR_FORMAT, disable=LOCAL_RANK > 0)
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pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
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for i, x in pbar:
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if cache_images == 'disk':
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b += self.npy_files[i].stat().st_size
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@ -612,7 +611,7 @@ class LoadImagesAndLabels(Dataset):
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pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
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desc=desc,
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total=len(self.im_files),
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bar_format=BAR_FORMAT)
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bar_format=TQDM_BAR_FORMAT)
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for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
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nm += nm_f
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nf += nf_f
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@ -50,6 +50,7 @@ NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiproces
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DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory
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AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
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VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode
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TQDM_BAR_FORMAT = '{l_bar}{bar:10}| {n_fmt}/{total_fmt} {elapsed}' # tqdm bar format
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FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
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torch.set_printoptions(linewidth=320, precision=5, profile='long')
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8
val.py
8
val.py
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@ -38,9 +38,9 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import DetectMultiBackend
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from utils.callbacks import Callbacks
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from utils.dataloaders import create_dataloader
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from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_yaml,
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coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
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scale_boxes, xywh2xyxy, xyxy2xywh)
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from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements,
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check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression,
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print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
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from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
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from utils.plots import output_to_target, plot_images, plot_val_study
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from utils.torch_utils import select_device, smart_inference_mode
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@ -193,7 +193,7 @@ def run(
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loss = torch.zeros(3, device=device)
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jdict, stats, ap, ap_class = [], [], [], []
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callbacks.run('on_val_start')
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pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
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pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
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for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
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callbacks.run('on_val_batch_start')
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with dt[0]:
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