学习笔记|Pytorch使用教程26(Normalizaiton_layers)

学习笔记|Pytorch使用教程26

本学习笔记主要摘自“深度之眼”，做一个总结，方便查阅。
使用Pytorch版本为1.2

• 为什么要Normalization ?
• 常见的Normalizaton——BN、 LN、IN and GN
• Normalization小结

一.为什么要Normalization ?

Internal Covariate Shift (ICS): 数据尺度/分布异常，导致训练困难

$\begin{aligned} \mathrm{H}_{11}=& \sum_{i=0}^{n} X_{i} * W_{1 i} \quad \mathrm{D}(\mathrm{X}+Y) \\ \mathrm{D}\left(\mathrm{H}_{11}\right) &=\sum_{i=0}^{n} D\left(X_{i}\right) * D\left(W_{1 i}\right) \\ &=n *(1 * 1) \\ &=n \\ \operatorname{std}\left(\mathrm{H}_{11}\right) &=\sqrt{\mathrm{D}\left(\mathrm{H}_{11}\right)}=\sqrt{\boldsymbol{n}} \end{aligned}$
$D(H_1) = n*D(X)*D(W)$
Normalization可以约束数据尺度，不会出现数据爆炸或者数据消失的情况，利于模型训练。

二.常见的Normalizaton——BN、 LN、IN and GN

1.Layer Normalization
起因: BN不适用于变长的网络,如RNN
思路:逐层计算均值和方差
注意事项:
1.不再有running_ mean和running var

• gamma和beta为逐元素的
  参考文献《Layer Normalization》
  

nn.LayerNorm

主要参数:

• normalized_shape: 该层特征形状
• eps:分母修正项
• elementwise_affine: 是否需要affine transform
  测试代码：

import torch
import numpy as np
import torch.nn as nn
from tools.common_tools import set_seed


set_seed(1)  # 设置随机种子

# ======================================== nn.layer norm
flag = 1
# flag = 0
if flag:
    batch_size = 2
    num_features = 3

    features_shape = (2, 2)

    feature_map = torch.ones(features_shape)  # 2D
    feature_maps = torch.stack([feature_map * (i + 1) for i in range(num_features)], dim=0)  # 3D
    feature_maps_bs = torch.stack([feature_maps for i in range(batch_size)], dim=0)  # 4D

    # feature_maps_bs shape is [8, 6, 3, 4],  B * C * H * W
    ln = nn.LayerNorm(feature_maps_bs.size()[1:], elementwise_affine=True)
    # ln = nn.LayerNorm(feature_maps_bs.size()[1:], elementwise_affine=False)
    # ln = nn.LayerNorm([6, 3, 4])
    # ln = nn.LayerNorm([6, 3])

    output = ln(feature_maps_bs)

    print("Layer Normalization")
    print(ln.weight.shape)
    print(feature_maps_bs[0, ...])
    print(output[0, ...])

输出：

Layer Normalization
torch.Size([3, 2, 2])
tensor([[[1., 1.],
         [1., 1.]],

        [[2., 2.],
         [2., 2.]],

        [[3., 3.],
         [3., 3.]]])
tensor([[[-1.2247, -1.2247],
         [-1.2247, -1.2247]],

        [[ 0.0000,  0.0000],
         [ 0.0000,  0.0000]],

        [[ 1.2247,  1.2247],
         [ 1.2247,  1.2247]]], grad_fn=<SelectBackward>)

如果设置：ln = nn.LayerNorm(feature_maps_bs.size()[1:], elementwise_affine=False)
则报错：AttributeError: 'NoneType' object has no attribute 'shape'
nn.LayerNorm()可以根据shape从后往前设置：# feature_maps_bs shape is [8, 6, 3, 4], B * C * H * W，可以设置nn.LayerNorm([4]),nn.LayerNorm([3,4])以及nn.LayerNorm([6,3,4])但不能设置nn.LayerNorm([6,3])

2.Instance Normalization
起因: BN在图像生成( Image Generation )中不适用
思路:逐Instance ( channel )计算均值和方差
《Instance Normalization: The Missing Ingredient for Fast Stylization》
《Image Style Transfer Using Convolutional Neural Networks》

nn.InstanceNorm

主要参数:

• num_features :一个样本特征数量(最重要)
• eps :分母修正项
• momentum :指数加权平均估计当前mean/var
• affine :是否需要affine transform
• track_running_stats :是训练状态，还是测试状态

测试代码

# ======================================== nn.instance norm 2d
flag = 1
# flag = 0
if flag:

    batch_size = 3
    num_features = 3
    momentum = 0.3

    features_shape = (2, 2)

    feature_map = torch.ones(features_shape)    # 2D
    feature_maps = torch.stack([feature_map * (i + 1) for i in range(num_features)], dim=0)  # 3D
    feature_maps_bs = torch.stack([feature_maps for i in range(batch_size)], dim=0)  # 4D

    print("Instance Normalization")
    print("input data:\n{} shape is {}".format(feature_maps_bs, feature_maps_bs.shape))

    instance_n = nn.InstanceNorm2d(num_features=num_features, momentum=momentum)

    for i in range(1):
        outputs = instance_n(feature_maps_bs)

        print(outputs)
        #print("\niter:{}, running_mean.shape: {}".format(i, bn.running_mean.shape))
        #print("iter:{}, running_var.shape: {}".format(i, bn.running_var.shape))
        #print("iter:{}, weight.shape: {}".format(i, bn.weight.shape))
        #print("iter:{}, bias.shape: {}".format(i, bn.bias.shape))

输出：

Instance Normalization
input data:
tensor([[[[1., 1.],
          [1., 1.]],

         [[2., 2.],
          [2., 2.]],

         [[3., 3.],
          [3., 3.]]],


        [[[1., 1.],
          [1., 1.]],

         [[2., 2.],
          [2., 2.]],

         [[3., 3.],
          [3., 3.]]],


        [[[1., 1.],
          [1., 1.]],

         [[2., 2.],
          [2., 2.]],

         [[3., 3.],
          [3., 3.]]]]) shape is torch.Size([3, 3, 2, 2])
tensor([[[[0., 0.],
          [0., 0.]],

         [[0., 0.],
          [0., 0.]],

         [[0., 0.],
          [0., 0.]]],


        [[[0., 0.],
          [0., 0.]],

         [[0., 0.],
          [0., 0.]],

         [[0., 0.],
          [0., 0.]]],


        [[[0., 0.],
          [0., 0.]],

         [[0., 0.],
          [0., 0.]],

         [[0., 0.],
          [0., 0.]]]])

3.Group Normalization
起因:小batch样本中，BN估计的值不准
思路:数据不够，通道来凑

注意事项:

• 1.不再有running_mean和running_var
• 2.gamma和beta为逐通道( channel )的
• 应用场景:大模型(小batch size)任务
• 《Group Normalization》

nn. GroupNorm

主要参数:

• num_groups:分组数
• num_channels: 通道数(特征数)
• eps:分母修正项
• affine:是否需要affine transform

测试代码：

# ======================================== nn.grop norm
flag = 1
# flag = 0
if flag:

    batch_size = 2
    num_features = 4
    num_groups = 2  # 3 Expected number of channels in input to be divisible by num_groups

    features_shape = (2, 2)

    feature_map = torch.ones(features_shape)    # 2D
    feature_maps = torch.stack([feature_map * (i + 1) for i in range(num_features)], dim=0)  # 3D
    feature_maps_bs = torch.stack([feature_maps * (i + 1) for i in range(batch_size)], dim=0)  # 4D

    gn = nn.GroupNorm(num_groups, num_features)
    outputs = gn(feature_maps_bs)

    print("Group Normalization")
    print(gn.weight.shape)
    print(outputs[0])

输出：

Group Normalization
torch.Size([4])
tensor([[[-1.0000, -1.0000],
         [-1.0000, -1.0000]],

        [[ 1.0000,  1.0000],
         [ 1.0000,  1.0000]],

        [[-1.0000, -1.0000],
         [-1.0000, -1.0000]],

        [[ 1.0000,  1.0000],
         [ 1.0000,  1.0000]]], grad_fn=<SelectBackward>)

三.Normalization小结