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Update TQDM bar format (#6988)

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Glenn Jocher 2022-03-15 16:32:56 +01:00 committed by GitHub
parent 932dc78496
commit c09fb2aa95
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2 changed files with 5 additions and 4 deletions
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2
utils/autoanchor.py
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@ -152,7 +152,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
# Evolve # Evolve
f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma 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: for _ in pbar:
v = np.ones(sh) v = np.ones(sh)
while (v == 1).all(): # mutate until a change occurs (prevent duplicates) while (v == 1).all(): # mutate until a change occurs (prevent duplicates)

7
utils/datasets.py
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@ -35,6 +35,7 @@ from utils.torch_utils import torch_distributed_zero_first
HELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' 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 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 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 # Get orientation exif tag
for orientation in ExifTags.TAGS.keys(): 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 nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total
if exists: if exists:
d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupt" 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']: if cache['msgs']:
LOGGER.info('\n'.join(cache['msgs'])) # display warnings 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}' 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 self.im_hw0, self.im_hw = [None] * n, [None] * n
fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
results = ThreadPool(NUM_THREADS).imap(fcn, range(n)) 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: for i, x in pbar:
if cache_images == 'disk': if cache_images == 'disk':
gb += self.npy_files[i].stat().st_size 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..." desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
with Pool(NUM_THREADS) as pool: with Pool(NUM_THREADS) as pool:
pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), 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: for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
nm += nm_f nm += nm_f
nf += nf_f nf += nf_f