49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
import torchvision.transforms as transforms
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import pcl.loader
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normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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def moco_v2():
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return [
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transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
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transforms.RandomApply([
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transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) # not strengthened
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], p=0.8),
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transforms.RandomGrayscale(p=0.2),
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transforms.RandomApply([pcl.loader.GaussianBlur([.1, 2.])], p=0.5),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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normalize
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]
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def moco_v1():
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return [
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transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
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transforms.RandomGrayscale(p=0.2),
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transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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normalize
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]
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def moco_eval():
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return transforms.Compose([
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transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
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# transforms.CenterCrop(512),
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transforms.ToTensor(),
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normalize
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])
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def only_rotate():
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return transforms.Compose([
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transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
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transforms.RandomRotation(90),
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transforms.ToTensor(),
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normalize
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])
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