pytorch归一化

数据维度为[N,C,H,W]

1、BatchNorm

归一化维度:[N,H,W] 计算C次均值方差

import torch
from torch import nn


#BatchNorm2d
input = torch.randn((8,32,32,3))
input_ = input.permute(0,3,1,2)
print(input_.shape)

BN = nn.BatchNorm2d(3)
output = BN(input_)
print(output[:,0,:,:])

#test
X = input_[:,0,:,:]  #N,H,W
print(X.shape)

mean = torch.mean(X)
var = torch.var(X)
BN_one = (input_[:,0,:,:] - mean) / torch.pow(var + BN.eps,0.5) * BN.weight[0] + BN.bias[0]

print("BN_one:",BN_one)

torch.Size([8, 3, 32, 32])
tensor([[[ 1.6338e-01, -2.38e-01, -1.2965e+00,  ...,  9.6178e-01,
          -4.1027e-01,  1.4072e+00],
         [-9.9580e-02, -8.1695e-02, -2.2693e-01,  ...,  1.1076e+00,
           2.3096e-01, -1.4278e+00],
         [ 1.2291e+00,  1.0623e+00, -5.4392e-01,  ..., -4.3424e-02,
          -2.2262e-01, -5.1729e-01],
		...,
         [-1.8724e+00,  5.1297e-01,  6.1065e-01,  ...,  1.4684e-01,
          -8.5345e-02, -1.9820e+00],
         [-1.8862e-02, -5.1397e-01, -8.9240e-01,  ...,  1.0580e+00,
           1.3407e+00,  2.6915e+00],
         [ 2.9983e-01, -8.8519e-01, -7.3509e-01,  ...,  3.3777e-01,
           4.3252e-01,  3.9348e-01]]], grad_fn=<SliceBackward0>)


torch.Size([8, 32, 32])
BN_one: tensor([[[ 1.6337e-01, -2.37e-01, -1.29e+00,  ...,  9.6173e-01,
          -4.1024e-01,  1.4071e+00],
         [-9.9574e-02, -8.1690e-02, -2.2692e-01,  ...,  1.1076e+00,
           2.3094e-01, -1.4277e+00],
         [ 1.2290e+00,  1.0623e+00, -5.4388e-01,  ..., -4.3422e-02,
          -2.2260e-01, -5.1726e-01],
         ..