I am a beginner to pytorch here. As I read the tutorial,  I always see such expression to normalization the input data.
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) 

However, if I understand correctly, this step basically do

input[channel] = (input[channel] - mean[channel]) / std[channel]

according to the documentation.
So the question is, in order to normalize an input image, why we just say the mean and std is 0.5 and 0.5? Shouldn’t we first calculate the mean & std of one whole image and then use that value to normalize? I mean, it doesn’t have to be the case that every image has the same mean and std. Do I miss something here?
Any comments and idea are highly appreciated! Thank you!