ultralytics 8.0.233 improve Classify train augmentations (#4546)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: andresinsitu <andres.rodriguez@ingenieriainsitu.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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13 changed files with 253 additions and 108 deletions
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@ -14,9 +14,14 @@ from ultralytics.utils.checks import check_version
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from ultralytics.utils.instance import Instances
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from ultralytics.utils.metrics import bbox_ioa
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from ultralytics.utils.ops import segment2box
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from ultralytics.utils.torch_utils import TORCHVISION_0_10, TORCHVISION_0_11, TORCHVISION_0_13
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from .utils import polygons2masks, polygons2masks_overlap
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DEFAULT_MEAN = (0.0, 0.0, 0.0)
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DEFAULT_STD = (1.0, 1.0, 1.0)
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DEFAULT_CROP_FTACTION = 1.0
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# TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
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class BaseTransform:
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@ -982,65 +987,144 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):
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# Classification augmentations -----------------------------------------------------------------------------------------
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def classify_transforms(size=224, rect=False, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)): # IMAGENET_MEAN, IMAGENET_STD
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"""Transforms to apply if albumentations not installed."""
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def classify_transforms(
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size=224,
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mean=DEFAULT_MEAN,
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std=DEFAULT_STD,
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interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
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crop_fraction: float = DEFAULT_CROP_FTACTION,
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):
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"""
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Classification transforms for evaluation/inference. Inspired by timm/data/transforms_factory.py.
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Args:
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size (int): image size
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mean (tuple): mean values of RGB channels
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std (tuple): std values of RGB channels
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interpolation (T.InterpolationMode): interpolation mode. default is T.InterpolationMode.BILINEAR.
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crop_fraction (float): fraction of image to crop. default is 1.0.
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Returns:
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T.Compose: torchvision transforms
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"""
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if isinstance(size, (tuple, list)):
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assert len(size) == 2
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scale_size = tuple([math.floor(x / crop_fraction) for x in size])
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else:
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scale_size = math.floor(size / crop_fraction)
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scale_size = (scale_size, scale_size)
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# aspect ratio is preserved, crops center within image, no borders are added, image is lost
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if scale_size[0] == scale_size[1]:
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# simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg)
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tfl = [T.Resize(scale_size[0], interpolation=interpolation)]
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else:
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# resize shortest edge to matching target dim for non-square target
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tfl = [T.Resize(scale_size)]
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tfl += [T.CenterCrop(size)]
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tfl += [T.ToTensor(), T.Normalize(
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mean=torch.tensor(mean),
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std=torch.tensor(std),
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)]
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return T.Compose(tfl)
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# Classification augmentations train ---------------------------------------------------------------------------------------
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def classify_augmentations(
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size=224,
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mean=DEFAULT_MEAN,
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std=DEFAULT_STD,
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scale=None,
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ratio=None,
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hflip=0.5,
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vflip=0.0,
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auto_augment=None,
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hsv_h=0.015, # image HSV-Hue augmentation (fraction)
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hsv_s=0.4, # image HSV-Saturation augmentation (fraction)
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hsv_v=0.4, # image HSV-Value augmentation (fraction)
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force_color_jitter=False,
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erasing=0.,
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interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
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):
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"""
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Classification transforms with augmentation for training. Inspired by timm/data/transforms_factory.py.
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Args:
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size (int): image size
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scale (tuple): scale range of the image. default is (0.08, 1.0)
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ratio (tuple): aspect ratio range of the image. default is (3./4., 4./3.)
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mean (tuple): mean values of RGB channels
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std (tuple): std values of RGB channels
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hflip (float): probability of horizontal flip
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vflip (float): probability of vertical flip
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auto_augment (str): auto augmentation policy. can be 'randaugment', 'augmix', 'autoaugment' or None.
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hsv_h (float): image HSV-Hue augmentation (fraction)
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hsv_s (float): image HSV-Saturation augmentation (fraction)
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hsv_v (float): image HSV-Value augmentation (fraction)
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contrast (float): image contrast augmentation (fraction)
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force_color_jitter (bool): force to apply color jitter even if auto augment is enabled
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erasing (float): probability of random erasing
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interpolation (T.InterpolationMode): interpolation mode. default is T.InterpolationMode.BILINEAR.
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Returns:
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T.Compose: torchvision transforms
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"""
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# Transforms to apply if albumentations not installed
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if not isinstance(size, int):
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raise TypeError(f'classify_transforms() size {size} must be integer, not (list, tuple)')
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transforms = [ClassifyLetterBox(size, auto=True) if rect else CenterCrop(size), ToTensor()]
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if any(mean) or any(std):
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transforms.append(T.Normalize(mean, std, inplace=True))
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return T.Compose(transforms)
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scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range
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ratio = tuple(ratio or (3. / 4., 4. / 3.)) # default imagenet ratio range
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primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)]
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if hflip > 0.:
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primary_tfl += [T.RandomHorizontalFlip(p=hflip)]
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if vflip > 0.:
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primary_tfl += [T.RandomVerticalFlip(p=vflip)]
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secondary_tfl = []
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disable_color_jitter = False
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if auto_augment:
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assert isinstance(auto_augment, str)
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# color jitter is typically disabled if AA/RA on,
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# this allows override without breaking old hparm cfgs
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disable_color_jitter = not force_color_jitter
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def hsv2colorjitter(h, s, v):
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"""Map HSV (hue, saturation, value) jitter into ColorJitter values (brightness, contrast, saturation, hue)"""
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return v, v, s, h
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def classify_albumentations(
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augment=True,
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size=224,
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scale=(0.08, 1.0),
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hflip=0.5,
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vflip=0.0,
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hsv_h=0.015, # image HSV-Hue augmentation (fraction)
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hsv_s=0.7, # image HSV-Saturation augmentation (fraction)
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hsv_v=0.4, # image HSV-Value augmentation (fraction)
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mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN
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std=(1.0, 1.0, 1.0), # IMAGENET_STD
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auto_aug=False,
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):
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"""YOLOv8 classification Albumentations (optional, only used if package is installed)."""
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prefix = colorstr('albumentations: ')
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try:
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import albumentations as A
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from albumentations.pytorch import ToTensorV2
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check_version(A.__version__, '1.0.3', hard=True) # version requirement
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if augment: # Resize and crop
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T = [A.RandomResizedCrop(height=size, width=size, scale=scale)]
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if auto_aug:
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# TODO: implement AugMix, AutoAug & RandAug in albumentations
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LOGGER.info(f'{prefix}auto augmentations are currently not supported')
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if auto_augment == 'randaugment':
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if TORCHVISION_0_11:
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secondary_tfl += [T.RandAugment(interpolation=interpolation)]
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else:
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if hflip > 0:
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T += [A.HorizontalFlip(p=hflip)]
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if vflip > 0:
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T += [A.VerticalFlip(p=vflip)]
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if any((hsv_h, hsv_s, hsv_v)):
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T += [A.ColorJitter(*hsv2colorjitter(hsv_h, hsv_s, hsv_v))] # brightness, contrast, saturation, hue
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else: # Use fixed crop for eval set (reproducibility)
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T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
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T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
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LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
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return A.Compose(T)
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LOGGER.warning('"auto_augment=randaugment" requires torchvision >= 0.11.0. Disabling it.')
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except ImportError: # package not installed, skip
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pass
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except Exception as e:
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LOGGER.info(f'{prefix}{e}')
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elif auto_augment == 'augmix':
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if TORCHVISION_0_13:
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secondary_tfl += [T.AugMix(interpolation=interpolation)]
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else:
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LOGGER.warning('"auto_augment=augmix" requires torchvision >= 0.13.0. Disabling it.')
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elif auto_augment == 'autoaugment':
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if TORCHVISION_0_10:
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secondary_tfl += [T.AutoAugment(interpolation=interpolation)]
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else:
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LOGGER.warning('"auto_augment=autoaugment" requires torchvision >= 0.10.0. Disabling it.')
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else:
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raise ValueError(f'Invalid auto_augment policy: {auto_augment}. Should be one of "randaugment", '
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f'"augmix", "autoaugment" or None')
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if not disable_color_jitter:
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secondary_tfl += [T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h)]
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final_tfl = [
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T.ToTensor(),
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T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)),
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T.RandomErasing(p=erasing, inplace=True)]
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return T.Compose(primary_tfl + secondary_tfl + final_tfl)
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# NOTE: keep this class for backward compatibility
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class ClassifyLetterBox:
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"""
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YOLOv8 LetterBox class for image preprocessing, designed to be part of a transformation pipeline, e.g.,
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@ -1091,6 +1175,7 @@ class ClassifyLetterBox:
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return im_out
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# NOTE: keep this class for backward compatibility
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class CenterCrop:
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"""YOLOv8 CenterCrop class for image preprocessing, designed to be part of a transformation pipeline, e.g.,
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T.Compose([CenterCrop(size), ToTensor()]).
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@ -1117,6 +1202,7 @@ class CenterCrop:
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return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
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# NOTE: keep this class for backward compatibility
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class ToTensor:
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"""YOLOv8 ToTensor class for image preprocessing, i.e., T.Compose([LetterBox(size), ToTensor()])."""
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@ -8,10 +8,11 @@ import cv2
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import numpy as np
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import torch
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import torchvision
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from PIL import Image
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from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable
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from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
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from .augment import Compose, Format, Instances, LetterBox, classify_augmentations, classify_transforms, v8_transforms
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from .base import BaseDataset
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from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image, verify_image_label
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@ -225,19 +226,17 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
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self.cache_disk = cache == 'disk'
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self.samples = self.verify_images() # filter out bad images
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self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im
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self.torch_transforms = classify_transforms(args.imgsz, rect=args.rect)
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self.album_transforms = classify_albumentations(
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augment=augment,
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size=args.imgsz,
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scale=(1.0 - args.scale, 1.0), # (0.08, 1.0)
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hflip=args.fliplr,
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vflip=args.flipud,
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hsv_h=args.hsv_h, # HSV-Hue augmentation (fraction)
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hsv_s=args.hsv_s, # HSV-Saturation augmentation (fraction)
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hsv_v=args.hsv_v, # HSV-Value augmentation (fraction)
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mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN
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std=(1.0, 1.0, 1.0), # IMAGENET_STD
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auto_aug=False) if augment else None
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scale = (1.0 - args.scale, 1.0) # (0.08, 1.0)
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self.torch_transforms = classify_augmentations(size=args.imgsz,
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scale=scale,
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hflip=args.fliplr,
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vflip=args.flipud,
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erasing=args.erasing,
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auto_augment=args.auto_augment,
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hsv_h=args.hsv_h,
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hsv_s=args.hsv_s,
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hsv_v=args.hsv_v) if augment else classify_transforms(
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size=args.imgsz, crop_fraction=args.crop_fraction)
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def __getitem__(self, i):
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"""Returns subset of data and targets corresponding to given indices."""
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@ -250,10 +249,9 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
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im = np.load(fn)
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else: # read image
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im = cv2.imread(f) # BGR
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if self.album_transforms:
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sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image']
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else:
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sample = self.torch_transforms(im)
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# Convert NumPy array to PIL image
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im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
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sample = self.torch_transforms(im)
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return {'img': sample, 'cls': j}
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def __len__(self) -> int:
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