Resnet-34框架

import torch
import torch.nn as nn
import torch.nn.functional as F

class ResidualBlock(nn.Module):

    '''
    实现子module: Residual Block
    '''

    def __init__(self,inchannel,outchannel,stride=1,shortcut=None):

        super(ResidualBlock,self).__init__()

        self.left=nn.Sequential(
            nn.Conv2d(inchannel,outchannel,3,stride,1,bias=False),
            nn.BatchNorm2d(outchannel),
            nn.ReLU(inplace=True),
            nn.Conv2d(outchannel,outchannel,3,1,1,bias=False),
            nn.BatchNorm2d(outchannel)
        )
        self.right=shortcut

    def forward(self,x):

        out=self.left(x)
        residual=x if self.right is None else self.right(x)
        out+=residual
        return F.relu(out)

class ResNet(nn.Module):

    '''
    实现主module：ResNet34
    ResNet34 包含多个layer，每个layer又包含多个residual block
    用子module来实现residual block，用_make_layer函数来实现layer
    '''

    def __init__(self,num_classes=1000):

        super(ResNet,self).__init__()

        # 前几层图像转换
        self.pre=nn.Sequential(
            nn.Conv2d(3,64,7,2,3,bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3,2,1)
        )

        # 重复的layer，分别有3，4，6，3个residual block
        self.layer1=self._make_layer(64,64,3)
        self.layer2=self._make_layer(64,128,4,stride=2)
        self.layer3=self._make_layer(128,256,6,stride=2)
        self.layer4=self._make_layer(256,512,3,stride=2)

        #分类用的全连接
        self.fc=nn.Linear(512,num_classes)

    def _make_layer(self,inchannel,outchannel,bloch_num,stride=1):

        '''
        构建layer,包含多个residual block
        '''
        shortcut=nn.Sequential(
            nn.Conv2d(inchannel,outchannel,1,stride,bias=False),
            nn.BatchNorm2d(outchannel)
        )
        layers=[]
        layers.append(ResidualBlock(inchannel,outchannel,stride,shortcut))
        for i in range(1,bloch_num):
            layers.append(ResidualBlock(outchannel,outchannel))
        return nn.Sequential(*layers)

    def forward(self,x):

        x=self.pre(x)

        x=self.layer1(x)
        x=self.layer2(x)
        x=self.layer3(x)
        x=self.layer4(x)

        x=F.avg_pool2d(x,7)
        x=x.view(x.size(0),-1)
        return self.fc(x)

if __name__ == '__main__':
    model=ResNet()
    # input=t.autograd.Variable(t.randn(1,3,224,224))
    input=t.autograd.Variable(t.randn(1,8,4,4))
    o=model(input)
    print(o)