pytorch实现特殊的Module--Sqeuential三种写法
我就废话不多说了，直接上代码吧！
# -*- coding: utf-8 -*-
#@Time :2019/7/1 13:34
#@Author :XiaoMa
import torch as t
from torch import nn
#Sequential的三种写法
net1=nn.Sequential()
net1.add_module('conv',nn.Conv2d(3,3,3)) #Conv2D(输入通道数，输出通道数，卷积核大小)
net1.add_module('batchnorm',nn.BatchNorm2d(3)) #BatchNorm2d(特征数)
net1.add_module('activation_layer',nn.ReLU())
net2=nn.Sequential(nn.Conv2d(3,3,3),
nn.BatchNorm2d(3),
nn.ReLU()
)
from collections import OrderedDict
net3=nn.Sequential(OrderedDict([
('conv1',nn.Conv2d(3,3,3)),
('bh1',nn.BatchNorm2d(3)),
('al',nn.ReLU())
]))
print('net1',net1)
print('net2',net2)
print('net3',net3)
#可根据名字或序号取出子module
print(net1.conv,net2[0],net3.conv1)
输出结果：
net1 Sequential( (conv): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) (batchnorm): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activation_layer): ReLU() ) net2 Sequential( (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) net3 Sequential( (conv1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) (bh1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (al): ReLU() ) Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
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