torchvision.transforms 数据预处理：Normalize()

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]]])
"""