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Remove NCOLS from tqdm (#5804) 

* Remove NCOLS from tqdm

* [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>
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Glenn Jocher 2021-11-26 13:37:28 +01:00 committed by GitHub
parent 53349dac8e
commit 7c6bae0ae6
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3 changed files with 8 additions and 8 deletions
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10
train.py
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@ -39,10 +39,10 @@ from utils.autobatch import check_train_batch_size
from utils.callbacks import Callbacks from utils.callbacks import Callbacks
from utils.datasets import create_dataloader from utils.datasets import create_dataloader
from utils.downloads import attempt_download from utils.downloads import attempt_download
from utils.general import (LOGGER, NCOLS, check_dataset, check_file, check_git_status, check_img_size, from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,
check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,
init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle,
one_cycle, print_args, print_mutation, strip_optimizer) print_args, print_mutation, strip_optimizer)
from utils.loggers import Loggers from utils.loggers import Loggers
from utils.loggers.wandb.wandb_utils import check_wandb_resume from utils.loggers.wandb.wandb_utils import check_wandb_resume
from utils.loss import ComputeLoss from utils.loss import ComputeLoss
@ -289,7 +289,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
pbar = enumerate(train_loader) pbar = enumerate(train_loader)
LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size')) LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
if RANK in [-1, 0]: if RANK in [-1, 0]:
pbar = tqdm(pbar, total=nb, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
optimizer.zero_grad() optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start) ni = i + nb * epoch # number integrated batches (since train start)

2
utils/general.py
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@ -838,4 +838,4 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
# Variables # Variables
NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns # terminal window size NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns # terminal window size for tqdm

4
val.py
View File

@ -26,7 +26,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend from models.common import DetectMultiBackend
from utils.callbacks import Callbacks from utils.callbacks import Callbacks
from utils.datasets import create_dataloader from utils.datasets import create_dataloader
from utils.general import (LOGGER, NCOLS, box_iou, check_dataset, check_img_size, check_requirements, check_yaml, from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,
coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
scale_coords, xywh2xyxy, xyxy2xywh) scale_coords, xywh2xyxy, xyxy2xywh)
from utils.metrics import ConfusionMatrix, ap_per_class from utils.metrics import ConfusionMatrix, ap_per_class
@ -164,7 +164,7 @@ 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 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) loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class = [], [], [], [] jdict, stats, ap, ap_class = [], [], [], []
pbar = tqdm(dataloader, desc=s, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
for batch_i, (im, targets, paths, shapes) in enumerate(pbar): for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
t1 = time_sync() t1 = time_sync()
if pt or engine: if pt or engine: