I am training my model on image crops of size 384x384 pixels.
Ideally, the full image should be passed through albumentations, including color normalization on a whole image. Then the preprocessing_fn should (or should not be applied). Then passed to training.
For inference, full image is taken, color norm and resizing is done, preprocessing_fn should (or should not be applied) is passed to the model for inference.
Do I need to use preprocessing_fn during training or during inference? I want to aviod an inconcistency that happens that color is normalized (or something else happens) on a crop during training, but on the full image during inference.

It seems that preprocessing_fn does the same as

color_norm = Compose([Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], )])
sample = color_norm(image=image)
color_norm_image = sample['image']