pytorch 创建网络的8种方法

import torch.nn as nn
from torch.nn import Conv2d
from collections import OrderedDict


class net1(nn.Module):
    def __init__(self):
        super(net1, self).__init__()
        self.conv1 = Conv2d(3, 3, 1)
        self.conv2 = Conv2d(3, 3, 1)

    def forward(self, x):
        return self.conv1(self.conv2(x))


net1 = net1()
print(net1)


class net2(nn.Module):
    def __init__(self):
        super(net2, self).__init__()
        self.layer = nn.Sequential(
            Conv2d(3, 3, 1),
            Conv2d(3, 3, 1)
        )

    def forward(self, x):
        return self.conv1(self.conv2(x))


net2 = net2()
print(net2)


class net3(nn.Module):
    def __init__(self):
        super(net3, self).__init__()
        self.layer = nn.Sequential(OrderedDict([
            ('conv1', Conv2d(3, 3, 1)),
            ('conv2', Conv2d(3, 3, 1))
        ]))

    def forward(self, x):
        return self.conv1(self.conv2(x))


net3 = net3()
print(net3)


class net4(nn.Module):
    def __init__(self):
        super(net4, self).__init__()
        self.layer = nn.Sequential()  # nn.ModuleDict(), nn.ModuleList()
        self.layer.add_module('conv1', Conv2d(3, 3, 3))
        self.layer.add_module('conv2', Conv2d(3, 3, 3))

    def forward(self, x):
        return self.conv1(self.conv2(x))


net4 = net4()
print(net4)


class net5(nn.Module):
    def __init__(self):
        super(net5, self).__init__()
        self.layer = nn.ModuleList([Conv2d(3, 3, 3), Conv2d(3, 3, 3)])

    def forward(self, x):
        return self.conv1(self.conv2(x))


net5 = net5()
print(net5)


class net6(nn.Module):
    def __init__(self):
        super(net6, self).__init__()
        self.layer = nn.ModuleDict({
            'conv1': Conv2d(3, 3, 3),
            'conv2': Conv2d(3, 3, 3)
        })

    def forward(self, x):
        return self.conv1(self.conv2(x))


net6 = net6()
print(net6)


class net7(nn.Module):
    def __init__(self):
        super(net7, self).__init__()
        self.layer = nn.ModuleDict([
            ('conv1', Conv2d(3, 3, 3)),
            ('conv2', Conv2d(3, 3, 3))
        ])

    def forward(self, x):
        return self.conv1(self.conv2(x))


net7 = net7()
print(net7)


class net8(nn.Module):
    def __init__(self):
        super(net8, self).__init__()
        self.list = [Conv2d(3, 3, 3), Conv2d(3, 3, 3)]  # 不建议使用， 不会加入到Module中

    def forward(self, x):
        return self.list(x)


model = net8()
print(model)

net1(
  (conv1): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
  (conv2): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
)
net2(
  (layer): Sequential(
    (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
    (1): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
  )
)
net3(
  (layer): Sequential(
    (conv1): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
    (conv2): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
  )
)
net4(
  (layer): Sequential(
    (conv1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
    (conv2): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  )
)
net5(
  (layer): ModuleList(
    (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
    (1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  )
)
net6(
  (layer): ModuleDict(
    (conv1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
    (conv2): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  )
)
net7(
  (layer): ModuleDict(
    (conv1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
    (conv2): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  )
)
net8()

Process finished with exit code 0