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ultralytics 8.0.105 classification hyp fix and new onplot callbacks (#2684)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ivan Shcheklein <shcheklein@gmail.com>
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Glenn Jocher 2023-05-17 19:10:20 +02:00 • committed by GitHub
parent b1119d512e
commit 23fc50641c
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92 changed files with 378 additions and 206 deletions
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16
ultralytics/yolo/data/augment.py
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@ -789,13 +789,20 @@ def classify_transforms(size=224, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)): #
return T.Compose([CenterCrop(size), ToTensor()])
def hsv2colorjitter(h, s, v):
"""Map HSV (hue, saturation, value) jitter into ColorJitter values (brightness, contrast, saturation, hue)"""
return v, v, s, h
def classify_albumentations(
augment=True,
size=224,
scale=(0.08, 1.0),
hflip=0.5,
vflip=0.0,
jitter=0.4,
hsv_h=0.015, # image HSV-Hue augmentation (fraction)
hsv_s=0.7, # image HSV-Saturation augmentation (fraction)
hsv_v=0.4, # image HSV-Value augmentation (fraction)
mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN
std=(1.0, 1.0, 1.0), # IMAGENET_STD
auto_aug=False,
@ -810,16 +817,15 @@ def classify_albumentations(
if augment: # Resize and crop
T = [A.RandomResizedCrop(height=size, width=size, scale=scale)]
if auto_aug:
# TODO: implement AugMix, AutoAug & RandAug in albumentation
# TODO: implement AugMix, AutoAug & RandAug in albumentations
LOGGER.info(f'{prefix}auto augmentations are currently not supported')
else:
if hflip > 0:
T += [A.HorizontalFlip(p=hflip)]
if vflip > 0:
T += [A.VerticalFlip(p=vflip)]
if jitter > 0:
jitter = float(jitter)
T += [A.ColorJitter(jitter, jitter, jitter, 0)] # brightness, contrast, saturation, 0 hue
if any((hsv_h, hsv_s, hsv_v)):
T += [A.ColorJitter(*hsv2colorjitter(hsv_h, hsv_s, hsv_v))] # brightness, contrast, saturation, hue
else: # Use fixed crop for eval set (reproducibility)
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor