serialize transforms into files.

serialize_transforms.py

import torch.package as package
import torch
import torchvision.transforms as T


def get_train_transforms(crop_size, mean, std, is_training):
    pipelines = []
    if is_training:
        pipelines.append(T.RandomResizedCrop(crop_size))
        pipelines.append(T.RandomHorizontalFlip())
    else:
        pipelines.append(T.Resize(int(crop_size/7*8)))
        pipelines.append(T.CenterCrop(crop_size))
    pipelines.append(T.ToTensor())
    pipelines.append(T.Normalize(mean=mean, std=std))
    return T.Compose(pipelines)

pre_processing = get_train_transforms(224, [0.5, 0.5, 0.5], [1, 1, 1], False)


with package.PackageExporter("pre_processing.pt.package") as exporter:
    exporter.intern("codebase.**")
    exporter.intern("torchvision.**")
    exporter.extern("numpy.**")
    exporter.extern("PIL.**")
    exporter.save_pickle("pre_processing", "transform.pkl", get_train_transforms)

importer = package.PackageImporter("pre_processing.pt.package")
f = importer.load_pickle("pre_processing", "transform.pkl")
pre_processing_pkl = f(224, [0.5, 0.5, 0.5], [1, 1, 1], False)

from PIL import Image
x = Image.open("img.png").convert("RGB")

y1 = pre_processing(x)
y2 = pre_processing_pkl(x)
print(torch.allclose(y1,y2))