记录PyTorch图像处理的归一化和标准化问题和tensor.sub_和tensor.sub方法

tensor.sub_(mean).div_(std)

手册里sub_()方法和sub()方法是一样的，给出的是
$${\text{out}}_i={\text{input}}_i-\text{alpha}\times {\text{others}}_i$$
这么分析的话normalize方法其实是单样本的Z-Score标准化

再谈到torch的归一化，其实在transforms方法的ToTensor方法中已经内嵌了归一化的过程，ToTensor方法的注释如下

'''Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. This transform does not support torchscript.

Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]'''