给我一份在pytorch框架下vgg16_unet的代码，其中编码部分加入三个跳接层来提取更多特征，解码部分加入编码到解码的跳跃连接，输出特征图维度为3

好的，以下是在PyTorch框架下实现vgg16_unet的代码，其中编码部分加入三个跳接层，解码部分加入编码到解码的跳跃连接，输出特征图维度为3：

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

class vgg16_unet(nn.Module):
    def __init__(self, n_classes=3):
        super(vgg16_unet, self).__init__()
        
        # VGG16 Encoder
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(128)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.bn5 = nn.BatchNorm2d(256)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn6 = nn.BatchNorm2d(256)
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn7 = nn.BatchNorm2d(256)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.bn8 = nn.BatchNorm2d(512)
        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn9 = nn.BatchNorm2d(512)
        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn10 = nn.BatchNorm2d(512)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn11 = nn.BatchNorm2d(512)
        self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn12 = nn.BatchNorm2d(512)
        self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn13 = nn.BatchNorm2d(512)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)

        # Decoder
        self.upconv1 = nn.ConvTranspose2d(512, 512, kernel_size=2, stride=2)
        self.conv14 = nn.Conv2d(512 + 512, 512, kernel_size=3, padding=1)
        self.bn14 = nn.BatchNorm2d(512)
        self.conv15 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn15 = nn.BatchNorm2d(512)
        self.conv16 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.bn16 = nn.BatchNorm2d(256)

        self.upconv2 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2)
        self.conv17 = nn.Conv2d(256 + 256, 256, kernel_size=3, padding=1)
        self.bn17 = nn.BatchNorm2d(256)
        self.conv18 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn18 = nn.BatchNorm2d(256)
        self.conv19 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.bn19 = nn.BatchNorm2d(128)

        self.upconv3 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2)
        self.conv20 = nn.Conv2d(128 + 128, 128, kernel_size=3, padding=1)
        self.bn20 = nn.BatchNorm2d(128)
        self.conv21 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.bn21 = nn.BatchNorm2d(64)

        self.upconv4 = nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2)
        self.conv22 = nn.Conv2d(64 + 64, 64, kernel_size=3, padding=1)
        self.bn22 = nn.BatchNorm2d(64)
        self.conv23 = nn.Conv2d(64, n_classes, kernel_size=1)

    def forward(self, x):
        # Encoder
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x1 = x.clone()
        x = self.pool1(x)

        x = F.relu(self.bn3(self.conv3(x)))
        x = F.relu(self.bn4(self.conv4(x)))
        x2 = x.clone()
        x = self.pool2(x)

        x = F.relu(self.bn5(self.conv5(x)))
        x = F.relu(self.bn6(self.conv6(x)))
        x = F.relu(self.bn7(self.conv7(x)))
        x3 = x.clone()
        x = self.pool3(x)

        x = F.relu(self.bn8(self.conv8(x)))
        x = F.relu(self.bn9(self.conv9(x)))
        x = F.relu(self.bn10(self.conv10(x)))
        x4 = x.clone()
        x = self.pool4(x)

        x = F.relu(self.bn11(self.conv11(x)))
        x = F.relu(self.bn12(self.conv12(x)))
        x = F.relu(self.bn13(self.conv13(x)))
        x5 = x.clone()
        x = self.pool5(x)

        # Decoder
        x = self.upconv1(x)
        x = torch.cat([x5, x], dim=1)
        x = F.relu(self.bn14(self.conv14(x)))
        x = F.relu(self.bn15(self.conv15(x)))
        x = F.relu(self.bn16(self.conv16(x)))

        x = self.upconv2(x)
        x = torch.cat([x4, x], dim=1)
        x = F.relu(self.bn17(self.conv17(x)))
        x = F.relu(self.bn18(self.conv18(x)))
        x = F.relu(self.bn19(self.conv19(x)))

        x = self.upconv3(x)
        x = torch.cat([x3, x], dim=1)
        x = F.relu(self.bn20(self.conv20(x)))
        x = F.relu(self.bn21(self.conv21(x)))

        x = self.upconv4(x)
        x = torch.cat([x2, x], dim=1)
        x = F.relu(self.bn22(self.conv22(x)))
        x = self.conv23(x)

        return x

这是一个简单的vgg16_unet模型实现，你可以根据自己的需要进行修改和优化。