目录

1.我搭建的是这一个

2.方法一，用一个sequential配置

2 方法二，用cfg进行配置

---

1.我搭建的是这一个

2.方法一，用一个sequential配置

import torch
from torch import nn
from torchinfo import summary

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer=nn.Sequential(
            nn.Conv2d(3,64,3,1,1),
            nn.Conv2d(64,64,3,1,1),
            nn.MaxPool2d(2),
            nn.Conv2d(64,128,3,1,1),
            nn.Conv2d(128,128,3,1,1),
            nn.MaxPool2d(2),
            nn.Conv2d(128,256,3,1,1),
            nn.Conv2d(256,256,3,1,1),
            nn.Conv2d(256,256,3,1,1),
            nn.MaxPool2d(2),
            nn.Conv2d(256,512,3,1,1),
            nn.Conv2d(512,512,3,1,1),
            nn.Conv2d(512,512,3,1,1),
            nn.MaxPool2d(2),
            nn.Conv2d(512, 512, 3, 1, 1),
            nn.Conv2d(512, 512, 3, 1, 1),
            nn.Conv2d(512, 512, 3, 1, 1),
            nn.MaxPool2d(2),

        )
        self.linear=nn.Sequential(
            nn.Flatten(1),
            nn.Linear(512 * 7 * 7, 4096),
            nn.Linear(4096, 4096),
            nn.Linear(4096, 1000),
            nn.Softmax(dim=1)
        )
    def forward(self,x):
        x=self.layer(x)
        x=self.linear(x)
        return x
if __name__ == '__main__':
    x=torch.randn(1,3,224,224)
    net=Net()
    out=net(x)
    print(out.shape)
    summary(net,(1,3,224,224))

summary是打印网络结构信息

2 方法二，用cfg进行配置

import cv2
import torch
from torch import nn
from torchinfo import summary
cfg={
    'vgg':[
        64,64,'M',
        128,128,'M',
        256,256,256,'M',
        512,512,512,'M',
        512,512,512,'M'
    ]
}
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        layer=[]
        out_ch=3
        for in_ch in cfg['vgg']:
            if in_ch=='M':
                layer+=[nn.MaxPool2d(2)]
            else:
                layer+=[nn.Conv2d(out_ch,in_ch,3,1,1)]
                out_ch=in_ch#容易出错
        self.layer=nn.Sequential(*layer)
        self.linear=nn.Sequential(
            nn.Flatten(),
            nn.Linear(512*7*7,4096),
            nn.Linear(4096,4096),
            nn.Linear(4096,1000)
        )
    def forward(self,x):
        x=self.layer(x)
        x=self.linear(x)
        return x
if __name__ == '__main__':
    x=torch.randn(1,3,224,224)
    net=Net()
    out=net(x)
    print(out.shape)