Python实现DenseU-Net：修改filters和优化前向传播

DenseU-Net简介

DenseU-Net是一种用于图像分割的深度学习模型，它结合了DenseNet和U-Net的优点，能够提取更丰富的特征，提高分割精度。

修改filters参数

在原始的DenseU-Net代码中，filters 参数的默认值通常为[64, 128, 256, 512]，我们可以根据需要修改该参数以调整模型的通道数。例如，将其修改为[64, 128, 256, 512, 1024]，可以增加模型的深度和复杂度。

修改后的DenseU_Net类的初始化函数如下所示：pythonclass DenseU_Net(nn.Module): def init(self, img_ch, output_ch, filters=[64, 128, 256, 512, 1024]): super(DenseU_Net, self).init() # ...

优化前向传播

为了使前向传播过程更加清晰易懂，我们可以对代码进行一些调整。以下代码展示了优化后的DenseU_Net类的前向传播函数：python def forward(self, x): # 初始卷积 x = self.Conv0(x) # 下采样路径 down = [x] for block in self.dens_blocks: x = block(x) down.append(x) x = self.Maxpool(x) # 中心块 center = self.conv_center_1(down[-1]) center = self.conv_center_2(center) center = self.drop_center(center)

    # 上采样路径        for i, up_conv in enumerate(self.up_convs):            x = up_conv(center)            x = torch.cat([down[-(i + 2)], x], dim=1)            x = self.dens_blocks[-(i + 2)](x)

    # 输出层        x = self.conv_final_1(x)        x = self.relu(x)        x = self.conv_final_2(x)

    return x

完整代码pythonimport torchimport torch.nn as nn

class Conv_Block(nn.Module): def init(self, ch_in, ch_out): super(Conv_Block, self).init() self.conv = nn.Sequential( nn.BatchNorm2d(ch_in), nn.ReLU(inplace=True), nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1) )

def forward(self, x):        return self.conv(x)

class Dense_Block(nn.Module): def init(self, ch_in, ch_out): super(Dense_Block, self).init() self.conv1 = Conv_Block(ch_in, ch_out) self.conv2 = Conv_Block(ch_out + ch_in, ch_out) self.conv3 = Conv_Block(ch_out * 2 + ch_in, ch_out)

def forward(self, input_tensor):        x1 = self.conv1(input_tensor)        add1 = torch.cat([x1, input_tensor], dim=1)        x2 = self.conv2(add1)        add2 = torch.cat([x1, input_tensor, x2], dim=1)        x3 = self.conv3(add2)        return x3

class Up_Conv(nn.Module): def init(self, ch_in, ch_out): super(Up_Conv, self).init() self.up = nn.Sequential( nn.Upsample(scale_factor=2), nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True), nn.BatchNorm2d(ch_out), nn.ReLU(inplace=True) )

def forward(self, x):        return self.up(x)

class DenseU_Net(nn.Module): def init(self, img_ch, output_ch, filters=[64, 128, 256, 512, 1024]): super(DenseU_Net, self).init()

    self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)        self.relu = nn.ReLU(inplace=True)

    self.Conv0 = nn.Conv2d(img_ch, filters[0], kernel_size=7, padding=3, stride=1)        self.dens_blocks = nn.ModuleList([            Dense_Block(filters[i], filters[i + 1]) for i in range(len(filters) - 1)        ])

    self.conv_center_1 = Conv_Block(filters[-1], filters[-1])        self.conv_center_2 = Conv_Block(filters[-1], filters[-1])        self.drop_center = nn.Dropout(0.5)

    self.up_convs = nn.ModuleList([            Up_Conv(filters[i + 1], filters[i]) for i in range(len(filters) - 1)        ])

    self.conv_final_1 = nn.Conv2d(filters[0], filters[0] // 2, 7, 1, 3)        self.conv_final_2 = nn.Conv2d(filters[0] // 2, output_ch, 3, 1, 1)

def forward(self, x):        # 初始卷积        x = self.Conv0(x)                # 下采样路径        down = [x]        for block in self.dens_blocks:            x = block(x)            down.append(x)            x = self.Maxpool(x)                # 中心块        center = self.conv_center_1(down[-1])        center = self.conv_center_2(center)        center = self.drop_center(center)

    # 上采样路径        for i, up_conv in enumerate(self.up_convs):            x = up_conv(center)            x = torch.cat([down[-(i + 2)], x], dim=1)            x = self.dens_blocks[-(i + 2)](x)

    # 输出层        x = self.conv_final_1(x)        x = self.relu(x)        x = self.conv_final_2(x)

    return x

总结

本文介绍了如何修改DenseU-Net模型的filters参数，并优化了前向传播代码。希望这些信息对您有所帮助！