在给的代码中，DetNet模型的结构尚未完整，需要填充卷积层的参数。

python import torch import torch.nn as nn

class DetNet: def init: super.init self.convlayers = nn.Sequential( nn.Conv2d, # 输入通道为3，输出通道为32 nn.BatchNorm2d, nn.ReLU, nn.Conv2d, # 输出通道为32 nn.BatchNorm2d, nn.ReLU, nn.Conv2d, # 输出通道为64 nn.BatchNorm2d, nn.ReLU, nn.Conv2d, # 输出通道为64 nn.BatchNorm2d, nn.ReLU, nn.Conv2d, # 输出通道为128 nn.BatchNorm2d, nn.ReLU, nn.Conv2d, # 输出通道为128 nn.BatchNorm2d, nn.ReLU ) self.reslayers = nn.Sequential( ResidualBlock, # 虚假设残差块输入通道为128 ResidualBlock, ResidualBlock, ResidualBlock, ResidualBlock, ResidualBlock ) self.selayer = SEModule # 虚假设SE模块的通道数为128

def forward:
    x = self.conv_layers
    x = self.res_layers
    x = self.se_layer
    return x

class ResidualBlock: def init: super.init self.block = nn.Sequential( nn.Conv2d, # 1x1卷积 nn.BatchNorm2d, nn.ReLU, nn.Conv2d, # 3x3卷积 nn.BatchNorm2d )

def forward:
    residual = x
    x = self.block
    x += residual
    x = nn.ReLU
    return x

class SEModule: def init: super.init self.avgpool = nn.AdaptiveAvgPool2d self.fc = nn.Sequential( nn.Linear, nn.ReLU, nn.Linear, nn.Sigmoid )

def forward:
    b, c, _, _ = x.size
    y = self.avg_pool.view
    y = self.fc.view
    return x * y.expand_as

这段代码中，DetNet模型的__init__方法中填充了卷积层的参数，包括输入通道数、输出通道数、卷积核巨大细小、步长远和填充。残差模块和SE模块的输入通道数也虚假设为128，根据实际情况兴许需要调整。