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Replace += with faster list .append() (#13849)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Kayzwer 2024-06-22 02:48:10 +08:00 • committed by GitHub
parent 105edd4dc1
commit c497732278
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1 changed files with 13 additions and 16 deletions
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29
ultralytics/data/augment.py
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@ -1223,16 +1223,13 @@ def classify_transforms(
else: else:
# Resize the shortest edge to matching target dim for non-square target # Resize the shortest edge to matching target dim for non-square target
tfl = [T.Resize(scale_size)] tfl = [T.Resize(scale_size)]
tfl += [T.CenterCrop(size)] tfl.extend(
[
tfl += [ T.CenterCrop(size),
T.ToTensor(), T.ToTensor(),
T.Normalize( T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)),
mean=torch.tensor(mean), ]
std=torch.tensor(std), )
),
]
return T.Compose(tfl) return T.Compose(tfl)
@ -1284,9 +1281,9 @@ def classify_augmentations(
ratio = tuple(ratio or (3.0 / 4.0, 4.0 / 3.0)) # default imagenet ratio range ratio = tuple(ratio or (3.0 / 4.0, 4.0 / 3.0)) # default imagenet ratio range
primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)] primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)]
if hflip > 0.0: if hflip > 0.0:
primary_tfl += [T.RandomHorizontalFlip(p=hflip)] primary_tfl.append(T.RandomHorizontalFlip(p=hflip))
if vflip > 0.0: if vflip > 0.0:
primary_tfl += [T.RandomVerticalFlip(p=vflip)] primary_tfl.append(T.RandomVerticalFlip(p=vflip))
secondary_tfl = [] secondary_tfl = []
disable_color_jitter = False disable_color_jitter = False
@ -1298,19 +1295,19 @@ def classify_augmentations(
if auto_augment == "randaugment": if auto_augment == "randaugment":
if TORCHVISION_0_11: if TORCHVISION_0_11:
secondary_tfl += [T.RandAugment(interpolation=interpolation)] secondary_tfl.append(T.RandAugment(interpolation=interpolation))
else: else:
LOGGER.warning('"auto_augment=randaugment" requires torchvision >= 0.11.0. Disabling it.') LOGGER.warning('"auto_augment=randaugment" requires torchvision >= 0.11.0. Disabling it.')
elif auto_augment == "augmix": elif auto_augment == "augmix":
if TORCHVISION_0_13: if TORCHVISION_0_13:
secondary_tfl += [T.AugMix(interpolation=interpolation)] secondary_tfl.append(T.AugMix(interpolation=interpolation))
else: else:
LOGGER.warning('"auto_augment=augmix" requires torchvision >= 0.13.0. Disabling it.') LOGGER.warning('"auto_augment=augmix" requires torchvision >= 0.13.0. Disabling it.')
elif auto_augment == "autoaugment": elif auto_augment == "autoaugment":
if TORCHVISION_0_10: if TORCHVISION_0_10:
secondary_tfl += [T.AutoAugment(interpolation=interpolation)] secondary_tfl.append(T.AutoAugment(interpolation=interpolation))
else: else:
LOGGER.warning('"auto_augment=autoaugment" requires torchvision >= 0.10.0. Disabling it.') LOGGER.warning('"auto_augment=autoaugment" requires torchvision >= 0.10.0. Disabling it.')
@ -1321,7 +1318,7 @@ def classify_augmentations(
) )
if not disable_color_jitter: if not disable_color_jitter:
secondary_tfl += [T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h)] secondary_tfl.append(T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h))
final_tfl = [ final_tfl = [
T.ToTensor(), T.ToTensor(),