使用nn.Sequential()对象和nn.ModuleList建立模型
【摘要】
1、使用nn.Sequential()建立模型的三种方式
import torch as tfrom torch import nn # Sequential的三种写法net1 = nn.Sequential()net1.add_module('conv', nn.Conv2d(3, 3, 3)) # Conv2D(输入...
1、使用nn.Sequential()建立模型的三种方式
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import torch as t
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from torch import nn
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# Sequential的三种写法
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net1 = nn.Sequential()
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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)
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net1.add_module('batchnorm', nn.BatchNorm2d(3)) # BatchNorm2d(特征数)
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net1.add_module('activation_layer', nn.ReLU())
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net2 = nn.Sequential(nn.Conv2d(3, 3, 3),
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nn.BatchNorm2d(3),
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nn.ReLU()
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)
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from collections import OrderedDict
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#注意字典的key不能重复
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net3 = nn.Sequential(OrderedDict([
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('conv1', nn.Conv2d(3, 3, 3)),
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('bh1', nn.BatchNorm2d(3)),
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('al', nn.ReLU())
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]))
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print('net1', net1)
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print('net2', net2)
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print('net3', net3)
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# 可根据名字或序号取出子module
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print(net1.conv, net2[0], net3.conv1)
输出：
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net1 Sequential(
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(conv): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
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(batchnorm): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(activation_layer): ReLU()
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)
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net2 Sequential(
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(0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
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(1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(2): ReLU()
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)
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net3 Sequential(
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(conv1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
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(bh1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(al): ReLU()
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)
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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。
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class MyModule(nn.Module):
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def __init__(self):
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super(MyModule, self).__init__()
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self.list = [nn.Linear(3, 4), nn.ReLU()]
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self.module_list = nn.ModuleList([nn.Conv2d(3, 3, 3), nn.ReLU()])
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def forward(self):
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pass
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model = MyModule()
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print(model)
输出：
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MyModule(
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(module_list): ModuleList(
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(0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
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(1): ReLU()
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)
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)
3、二者结合构造更复杂的网络模型
例如cisnet中的Decoder模型
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class Decoder(nn.Module):
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num_quan_bits = 4
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def __init__(self, feedback_bits):
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super(Decoder, self).__init__()
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self.feedback_bits = feedback_bits
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self.dequantize = DequantizationLayer(self.num_quan_bits)
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self.multiConvs = nn.ModuleList()
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self.fc = nn.Linear(int(feedback_bits / self.num_quan_bits), 768)
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self.out_cov = conv3x3(2, 2)
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self.sig = nn.Sigmoid()
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for _ in range(3):
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self.multiConvs.append(nn.Sequential(
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conv3x3(2, 8),
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nn.ReLU(),
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conv3x3(8, 16),
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nn.ReLU(),
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conv3x3(16, 2),
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nn.ReLU()))
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def forward(self, x):
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out = self.dequantize(x)
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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
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out = self.sig(self.fc(out))
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out = out.contiguous().view(-1, 2, 24, 16) #需使用contiguous().view(),或者可修改为reshape
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for i in range(3):
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residual = out
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out = self.multiConvs[i](out)
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out = residual + out
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out = self.out_cov(out)
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out = self.sig(out)
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out = out.permute(0, 2, 3, 1)
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return out
文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。
原文链接：wanghao.blog.csdn.net/article/details/114476467
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