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import torch
from torchvision import transforms as transforms
from torchvision.transforms import Compose
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
def make_test_transforms(image_size):
test_transforms: Compose = transforms.Compose([
transforms.Resize(size=image_size, antialias=True),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD)
])
return test_transforms
def inverse_normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
mean = torch.as_tensor(mean)
std = torch.as_tensor(std)
un_normalize = transforms.Normalize((-mean / std).tolist(), (1.0 / std).tolist())
return un_normalize
def normalize_only(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
normalize = transforms.Normalize(mean=mean, std=std)
return normalize
def inverse_normalize_w_resize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
resize_resolution=(256, 256)):
mean = torch.as_tensor(mean)
std = torch.as_tensor(std)
resize_unnorm = transforms.Compose([
transforms.Normalize((-mean / std).tolist(), (1.0 / std).tolist()),
transforms.Resize(size=resize_resolution, antialias=True)])
return resize_unnorm
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