preprocessing.py

import torch


def norm_max(tensor: torch.Tensor):
    return (tensor - tensor.min()) / (tensor.max() - tensor.min())


class MaxNorm:
    def __call__(self, tensor: torch.Tensor):
        return norm_max(tensor)


def norm_mean(tensor: torch.Tensor):
    return tensor / tensor.mean()


class MeanNorm:
    def __call__(self, tensor: torch.Tensor):
        return norm_mean(tensor)


def norm_gaussian(tensor: torch.Tensor):
    return (tensor - tensor.mean()) / tensor.std()


class GaussianNorm:
    def __call__(self, tensor: torch.Tensor):
        return norm_gaussian(tensor)


__all__ = [
    norm_max.__name__,
    norm_mean.__name__,
    norm_gaussian.__name__,
    MaxNorm.__name__,
    MeanNorm.__name__,
    GaussianNorm.__name__,
]