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    pytorch实现特殊的Module--Sqeuential三种写法

    栏目:代码类 时间:2020-01-15 15:06

    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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