给我一份在pytorch框架下vgg16_unet的代码，输出特征图维度为3

以下是基于PyTorch框架实现的VGG16_UNET代码：

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

class VGG16_UNET(nn.Module):
    def __init__(self, num_classes=3):
        super(VGG16_UNET, self).__init__()
        self.num_classes = num_classes
        # Encoder
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        # Decoder
        self.upconv6 = nn.ConvTranspose2d(512, 512, kernel_size=2, stride=2)
        self.conv6_1 = nn.Conv2d(1024, 512, kernel_size=3, padding=1)
        self.conv6_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv6_3 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.upconv7 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2)
        self.conv7_1 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.conv7_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv7_3 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.upconv8 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2)
        self.conv8_1 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.conv8_2 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.upconv9 = nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2)
        self.conv9_1 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.conv9_2 = nn.Conv2d(64, self.num_classes, kernel_size=3, padding=1)

    def forward(self, x):
        # Encoder
        x = F.relu(self.conv1_1(x))
        x = F.relu(self.conv1_2(x))
        x = self.pool1(x)
        x = F.relu(self.conv2_1(x))
        x = F.relu(self.conv2_2(x))
        x = self.pool2(x)
        x = F.relu(self.conv3_1(x))
        x = F.relu(self.conv3_2(x))
        x = F.relu(self.conv3_3(x))
        x = self.pool3(x)
        x = F.relu(self.conv4_1(x))
        x = F.relu(self.conv4_2(x))
        x = F.relu(self.conv4_3(x))
        x = self.pool4(x)
        x = F.relu(self.conv5_1(x))
        x = F.relu(self.conv5_2(x))
        x = F.relu(self.conv5_3(x))
        x = self.pool5(x)
        # Decoder
        x = self.upconv6(x)
        x = torch.cat((x, F.relu(self.conv4_3(x))), dim=1)
        x = F.relu(self.conv6_1(x))
        x = F.relu(self.conv6_2(x))
        x = F.relu(self.conv6_3(x))
        x = self.upconv7(x)
        x = torch.cat((x, F.relu(self.conv3_3(x))), dim=1)
        x = F.relu(self.conv7_1(x))
        x = F.relu(self.conv7_2(x))
        x = F.relu(self.conv7_3(x))
        x = self.upconv8(x)
        x = torch.cat((x, F.relu(self.conv2_2(x))), dim=1)
        x = F.relu(self.conv8_1(x))
        x = F.relu(self.conv8_2(x))
        x = self.upconv9(x)
        x = torch.cat((x, F.relu(self.conv1_2(x))), dim=1)
        x = F.relu(self.conv9_1(x))
        x = self.conv9_2(x)
        return x

这个代码使用了VGG16作为Encoder，使用transpose convolution实现Decoder，并输出特征图维度为3。如果你需要更改输出维度，只需要修改`num_classes`的值即可。