1、Normalize() 的作用
Normalize() 是pytorch中的数据预处理函数，包含在 torchvision.transforms 模块下。一般用于处理图像数据，其输入数据格式是 torch.Tensor，而不是 np.array。
1.1 Normalize() 的源码
看一下 Normalize() 函数的源码：
class Normalize(torch.nn.Module):
"""Normalize a tensor image with mean and standard deviation.
This transform does not support PIL Image.
Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
channels, this transform will normalize each channel of the input
``torch.*Tensor`` i.e.,
``output[channel] = (input[channel] - mean[channel]) / std[channel]``
.. note::
This transform acts out of place, i.e., it does not mutate the input tensor.
Args:
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
inplace(bool,optional): Bool to make this operation in-place.
"""
大意是：使用均值和标准差对输入的tensor的每个通道进行标准化，计算公式是：
output[channel] = (input[channel] - mean[channel]) / std[channel]
这里要与正态分布标准化进行区分，将一个正态分布转化为标准正太分布（即高斯分布）的公式为 Z=(X-mean)/var，这里的分母是方差而不是标准差。
1.2 代码示例
这里用代码来演示一下Normalize()的作用：
import numpy as np
from torchvision import transforms
data = np.array([
[0., 5, 10, 20, 0],
[255, 125, 180, 255, 196]
]) # 因为 Normalize() 的输入必须是 float 类型，所以这里定义一个 np.float64类型的 array
tensor = transforms.ToTensor()(data)
norm = transforms.Normalize((0.5), (0.5)) # mean=0.5 std=0.5
print(f"tensor = {
tensor}")
print(f"norm(tensor) = {
norm(tensor)}")
"""
tensor = tensor([[[ 0., 5., 10., 20., 0.],
[255., 125., 180., 255., 196.]]], dtype=torch.float64)
norm(tensor) = tensor([[[ -1., 9., 19., 39., -1.],
[509., 249., 359., 509., 391.]]], dtype=torch.float64)
"""
很容易可以验证：
(0 - 0.5) / 0.5 = -1
(5 - 0.5) / 0.5 = 9
(255 - 0.5) / 0.5 = 509
2、ToTensor() 和 Normalize() 的结合使用
在图像预处理中，Normalize() 通常和 ToTensor() 一起使用。 ToTensor() 的介绍可以参考 torchvision.transforms 数据预处理：ToTensor()
首先 ToTensor() 将 [0,255] 的像素值归一化为 [0,1]，然后使用 Normalize(0.5, 0.5) 将 [0,1] 进行标准化为 [-1,1]
ToTensor() 和Normalize() 结合使用的代码示例：
import numpy as np
from torchvision import transforms
data = np.array([
[0, 5, 10, 20, 0],
[255, 125, 180, 255, 196]
], dtype=np.uint8)
tensor = transforms.ToTensor()(data)
norm = transforms.Normalize(0.5, 0.5)
print(f"tensor = {
tensor}")
print(f"norm(tensor) = {
norm(tensor)}")
"""
tensor = tensor([[[0.0000, 0.0196, 0.0392, 0.0784, 0.0000],
[1.0000, 0.4902, 0.7059, 1.0000, 0.7686]]])
norm(tensor) = tensor([[[-1.0000, -0.9608, -0.9216, -0.8431, -1.0000],
[ 1.0000, -0.0196, 0.4118, 1.0000, 0.5373]]])
"""
使用 transforms.Compose() 函数进行图像预处理：
from torchvision import transforms
import cv2
filePath = "Dataset/FFHQ/00000.png"
img = cv2.imread(filePath)
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
img = transform(img)
print(img)
"""
tensor([[[ 0.1451, 0.1294, 0.1059, ..., 0.2157, 0.2000, 0.1843],
[ 0.1529, 0.1137, 0.1294, ..., 0.1843, 0.1843, 0.1922],
[ 0.1216, 0.1137, 0.1529, ..., 0.2314, 0.1686, 0.1529],
...,
[-0.8118, -0.7961, -0.7725, ..., 0.0980, 0.0824, 0.0588],
[-0.8196, -0.8196, -0.8039, ..., 0.0588, 0.0353, 0.0275],
[-0.8667, -0.8510, -0.8275, ..., 0.0431, 0.0431, 0.0510]]])
"""