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ultralytics 8.0.169TQDM, INTERP_LINEAR and RTDETR load_image() updates (#4704)

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Co-authored-by: Rustem Galiullin <rustemgal@gmail.com>
Co-authored-by: Rustem Galiullin <rustem.galiullin@bayanat.ai>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-09-02 20:01:57 +02:00 • committed by GitHub
parent a4fabfdacf
commit 187b504d68
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23 changed files with 101 additions and 120 deletions
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13
ultralytics/data/dataset.py
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@ -8,9 +8,8 @@ import cv2
import numpy as np
import torch
import torchvision
from tqdm import tqdm
from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, colorstr, is_dir_writeable
from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable
from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
from .base import BaseDataset
@ -60,7 +59,7 @@ class YOLODataset(BaseDataset):
iterable=zip(self.im_files, self.label_files, repeat(self.prefix),
repeat(self.use_keypoints), repeat(len(self.data['names'])), repeat(nkpt),
repeat(ndim)))
pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT)
pbar = TQDM(results, desc=desc, total=total)
for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar:
nm += nm_f
nf += nf_f
@ -107,7 +106,7 @@ class YOLODataset(BaseDataset):
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=self.prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display results
TQDM(None, desc=self.prefix + d, total=n, initial=n) # display results
if cache['msgs']:
LOGGER.info('\n'.join(cache['msgs'])) # display warnings
@ -244,7 +243,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
im = self.samples[i][3] = cv2.imread(f)
elif self.cache_disk:
if not fn.exists(): # load npy
np.save(fn.as_posix(), cv2.imread(f))
np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
im = np.load(fn)
else: # read image
im = cv2.imread(f) # BGR
@ -269,7 +268,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
nf, nc, n, samples = cache.pop('results') # found, missing, empty, corrupt, total
if LOCAL_RANK in (-1, 0):
d = f'{desc} {nf} images, {nc} corrupt'
tqdm(None, desc=d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)
TQDM(None, desc=d, total=n, initial=n)
if cache['msgs']:
LOGGER.info('\n'.join(cache['msgs'])) # display warnings
return samples
@ -278,7 +277,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
nf, nc, msgs, samples, x = 0, 0, [], [], {}
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
pbar = tqdm(results, desc=desc, total=len(self.samples), bar_format=TQDM_BAR_FORMAT)
pbar = TQDM(results, desc=desc, total=len(self.samples))
for sample, nf_f, nc_f, msg in pbar:
if nf_f:
samples.append(sample)