Resnet读代码

Resnet34

Residual Block

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)
        #这里应该是解决经过网络后维度不匹配的问题，唯独不匹配已经通过shortcut解决了？
        #我猜测
        #对，就是这样的，关于shortcut部分已经处理了维度不匹配的问题。
        residual = x if self.right is None else self.right(x)
        out += residual
        return F.relu(out)

Resnet34

class ResNet34(BasicModule):
    '''
    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=2):
        super(ResNet34, self).__init__()
        self.model_name = 'resnet34'

        # 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, 128, 3)
        self.layer2 = self._make_layer( 128, 256, 4, stride=2)
        self.layer3 = self._make_layer( 256, 512, 6, stride=2)
        self.layer4 = self._make_layer( 512, 512, 3, stride=2)

        #分类用的全连接
        self.fc = nn.Linear(512, num_classes)
    
    def _make_layer(self,  inchannel, outchannel, block_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))
        		#batchnorm使得输出维数与之前本来要输出的维度匹配，相同。
        
        layers = []
        layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))
        
        for i in range(1, block_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)