# 50、101、152层残差块，三个卷积层，1*1，3*3，1*1
class Bottleneck(nn.Module):
    # 这里对应是4,对应每层中的64，64，256
    expansion=4
    def __init__(self,in_channel,out_channel,stride=1,downsample=None):
        super(Bottleneck,self).__init__()
        self.conv1=nn.Conv2d(in_channels=in_channel,out_channels=out_channel,
                             kernel_size=1,stride=1,bias=False)
        self.bn1=nn.BatchNorm2d(out_channel)
        self.conv2=nn.Conv2d(in_channels=out_channel,out_channels=out_channel,
                             kernel_size=3,stride=stride,padding=1,bias=False)
        self.bn2=nn.BatchNorm2d(out_channel)
        self.conv3=nn.Conv2d(in_channels=out_channel,out_channels=out_channel*self.expansion,
                             kernel_size=1,stride=1,bias=False)
        self.bn3=nn.BatchNorm2d(out_channel*self.expansion)
        self.relu=nn.ReLU(inplace=True)
        self.downsample=downsample
    def forward(self,x):
        identity=x
        if self.downsample is not None:
            identity=downsample(x)
        out=self.conv1(x)
        out=self.bn1(out)
        out=self.relu(out)
        out=self.conv2(out)
        out=self.bn2(out)
        out=self.relu(out)
        out=self.conv3(out)
        out=self.bn3(out)
        out+=identity
        out=self.relu(out)
        return out