utils/transform_utils.py

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