my_models/clip_model/data_loader.py

import torchvision.transforms as T
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


pre_process = T.Compose(
    [
        T.ToPILImage(),
        T.Resize(
            size=(224, 224),
            interpolation=T.InterpolationMode.BICUBIC,
            antialias=True,
        ),
        T.ToTensor(),
        T.Normalize(
            mean=(0.48145466, 0.4578275, 0.40821073),
            std=(0.26862954, 0.26130258, 0.27577711),
        ),
    ]
)


def pre_process_foo(img_size: tuple, dataset: str = "laion") -> T.Compose:
    return T.Compose(
        [
            T.ToPILImage(),
            T.Resize(
                size=img_size,
                interpolation=T.InterpolationMode.BICUBIC,
                antialias=True,
            ),
            T.ToTensor(),
            T.Normalize(
                mean=(0.48145466, 0.4578275, 0.40821073)
                if dataset != "imagenet"
                else IMAGENET_DEFAULT_MEAN,
                std=(0.26862954, 0.26130258, 0.27577711)
                if dataset != "imagenet"
                else IMAGENET_DEFAULT_STD,
            ),
        ]
    )