使用nn.Sequential()对象和nn.ModuleList建立模型

骑猪看日落 2022-11-06 00:50 164阅读 0赞

1、使用nn.Sequential()建立模型的三种方式

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
    #注意字典的key不能重复
    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：

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))

# 2、使用nn.ModuleList建立模型。 #

class MyModule(nn.Module):
        def __init__(self):
            super(MyModule, self).__init__()
            self.list = [nn.Linear(3, 4), nn.ReLU()]
            self.module_list = nn.ModuleList([nn.Conv2d(3, 3, 3), nn.ReLU()])
    
        def forward(self):
            pass
    
    
    model = MyModule()
    print(model)

输出：

MyModule(
      (module_list): ModuleList(
        (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
        (1): ReLU()
      )
    )

3、二者结合构造更复杂的网络模型

例如cisnet中的Decoder模型

class Decoder(nn.Module):
        num_quan_bits = 4
        def __init__(self, feedback_bits):
            super(Decoder, self).__init__()
            self.feedback_bits = feedback_bits
            self.dequantize = DequantizationLayer(self.num_quan_bits)
            self.multiConvs = nn.ModuleList()
            self.fc = nn.Linear(int(feedback_bits / self.num_quan_bits), 768)
            self.out_cov = conv3x3(2, 2)
            self.sig = nn.Sigmoid()
            for _ in range(3):
                self.multiConvs.append(nn.Sequential(
                    conv3x3(2, 8),
                    nn.ReLU(),
                    conv3x3(8, 16),
                    nn.ReLU(),
                    conv3x3(16, 2),
                    nn.ReLU()))
        def forward(self, x):
            out = self.dequantize(x)
            out = out.contiguous().view(-1, int(self.feedback_bits / self.num_quan_bits)) #需使用contiguous().view(),或者可修改为reshape
            out = self.sig(self.fc(out))
            out = out.contiguous().view(-1, 2, 24, 16) #需使用contiguous().view(),或者可修改为reshape
            for i in range(3):
                residual = out
                out = self.multiConvs[i](out)
                out = residual + out
            out = self.out_cov(out)
            out = self.sig(out)
            out = out.permute(0, 2, 3, 1)
            return out