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data.py
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data.py
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import random
import torchvision.transforms as transforms
from PIL import Image, ImageOps, ImageFilter
from timm.data.transforms_factory import create_transform
def get_transforms(config):
if config.transform_config_str:
return TimmTransform()
else:
return Transform()
class TimmTransform:
def __init__(self, config, train=True):
self.config = config
self.train = train
self.train_transform = create_transform(
224, is_training=True, auto_augment=config.transform_config_str
)
self.eval_transform = create_transform(224, is_training=False)
self.transform = self.train_transform if self.train else self.eval_transform
def __call__(self, x):
y1 = self.transform(x)
y2 = self.transform(x)
return y1, y2
class GaussianBlur(object):
def __init__(self, p):
self.p = p
def __call__(self, img):
if random.random() < self.p:
sigma = random.random() * 1.9 + 0.1
return img.filter(ImageFilter.GaussianBlur(sigma))
else:
return img
class Solarization(object):
def __init__(self, p):
self.p = p
def __call__(self, img):
if random.random() < self.p:
return ImageOps.solarize(img)
else:
return img
class Transform:
def __init__(self):
self.transform = transforms.Compose(
[
transforms.RandomResizedCrop(224, interpolation=Image.BICUBIC),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply(
[
transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1
)
],
p=0.8,
),
transforms.RandomGrayscale(p=0.2),
GaussianBlur(p=1.0),
Solarization(p=0.0),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
self.transform_prime = transforms.Compose(
[
transforms.RandomResizedCrop(224, interpolation=Image.BICUBIC),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply(
[
transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1
)
],
p=0.8,
),
transforms.RandomGrayscale(p=0.2),
GaussianBlur(p=0.1),
Solarization(p=0.2),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
def __call__(self, x):
y1 = self.transform(x)
y2 = self.transform_prime(x)
return y1, y2