# The :class:`~torchvision.transforms.RandomPosterize` transform # (see also :func:`~torchvision.transforms.functional.posterize`) # randomly posterizes the image by reducing the number of bits # of each color channel. posterizer = T.RandomPosterize(bits=2) posterized_imgs = [posterizer(orig_img) for _ in range(4)] plot(posterized_imgs) #################################### # RandomSolarize # ~~~~~~~~~~~~~~ # The :class:`~torchvision.transforms.RandomSolarize` transform # (see also :func:`~torchvision.transforms.functional.solarize`) # randomly solarizes the image by inverting all pixel values above # the threshold. solarizer = T.RandomSolarize(threshold=192.0) solarized_imgs = [solarizer(orig_img) for _ in range(4)] plot(solarized_imgs) #################################### # RandomAdjustSharpness # ~~~~~~~~~~~~~~~~~~~~~ # The :class:`~torchvision.transforms.RandomAdjustSharpness` transform # (see also :func:`~torchvision.transforms.functional.adjust_sharpness`) # randomly adjusts the sharpness of the given image. sharpness_adjuster = T.RandomAdjustSharpness(sharpness_factor=2) sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)] plot(sharpened_imgs) #################################### # RandomAutocontrast
def __init__(
self,
global_crops_scale=(0.4, 1),
local_crops_scale=(0.05, 0.4),
n_local_crops=8,
size=224,
):
self.n_local_crops = n_local_crops
RandomGaussianBlur = lambda p: transforms.RandomApply( # noqa
[transforms.GaussianBlur(kernel_size=5, sigma=(0.1, 2))],
p=p,
)
flip_and_jitter = transforms.Compose(
[
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply(
[
transforms.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.2,
hue=0.1,
),
]
),
transforms.RandomGrayscale(p=0.2),
]
)
normalize = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
]
)
self.global_1 = transforms.Compose(
[
transforms.RandomResizedCrop(
size,
scale=global_crops_scale,
interpolation=Image.BICUBIC,
),
flip_and_jitter,
RandomGaussianBlur(1.0), # always apply
normalize,
],
)
self.global_2 = transforms.Compose(
[
transforms.RandomResizedCrop(
size,
scale=global_crops_scale,
interpolation=Image.BICUBIC,
),
flip_and_jitter,
RandomGaussianBlur(0.1),
transforms.RandomSolarize(170, p=0.2),
normalize,
],
)
self.local = transforms.Compose(
[
transforms.RandomResizedCrop(
size,
scale=local_crops_scale,
interpolation=Image.BICUBIC,
),
flip_and_jitter,
RandomGaussianBlur(0.5),
normalize,
],
)