Normalize.py

import torch
import torch.nn as nn


class Normalize(nn.Module):

    def __init__(self, mean, std):
        super(Normalize, self).__init__()
        self.mean = mean
        self.std = std

    def forward(self, input):
        size = input.size()
        x = input.clone()
        for i in range(size[1]):
            x[:, i] = (x[:, i] - self.mean[i]) / self.std[i]
        return x

class TfNormalize(nn.Module):

    def __init__(self, mean=0, std=1, mode='tensorflow'):
        """
        mode:
            'tensorflow':convert data from [0,1] to [-1,1]
            'torch':(input - mean) / std
        """
        super(TfNormalize, self).__init__()
        self.mean = mean
        self.std = std
        self.mode = mode

    def forward(self, input):
        size = input.size()
        x = input.clone()

        if self.mode == 'tensorflow':
            x = x * 2.0 - 1.0  # convert data from [0,1] to [-1,1]
        elif self.mode == 'torch':
            for i in range(size[1]):
                x[:, i] = (x[:, i] - self.mean[i]) / self.std[i]
        return x


class Permute(nn.Module):
    def __init__(self, permutation=[2, 1, 0]):
        super().__init__()
        self.permutation = permutation

    def forward(self, input):
        return input[:, self.permutation]