帮我写出PyTorch框架实现的VGG16_UNET的代码，其中最初输入图像大小为256x256，最终输出特征图大小为256x256，通道数为3

好的，以下是基于PyTorch框架实现的VGG16_UNET代码：

import torch
import torch.nn as nn

class VGG16_UNET(nn.Module):
    def __init__(self):
        super(VGG16_UNET, self).__init__()
        # Encoder
        self.encoder_block1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_block2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_block3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_block4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_block5 = nn.Sequential(
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        # Decoder
        self.decoder_block1 = nn.Sequential(
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        )
        self.decoder_block2 = nn.Sequential(
            nn.Conv2d(512, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        )
        self.decoder_block3 = nn.Sequential(
            nn.Conv2d(256, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        )
        self.decoder_block4 = nn.Sequential(
            nn.Conv2d(128, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 3, kernel_size=1)
        )

    def forward(self, x):
        # Encoder
        x1 = self.encoder_block1(x)
        x2 = self.encoder_block2(x1)
        x3 = self.encoder_block3(x2)
        x4 = self.encoder_block4(x3)
        x5 = self.encoder_block5(x4)
        # Decoder
        d1 = self.decoder_block1(x5)
        d1 = torch.cat([x4, d1], dim=1)
        d2 = self.decoder_block2(d1)
        d2 = torch.cat([x3, d2], dim=1)
        d3 = self.decoder_block3(d2)
        d3 = torch.cat([x2, d3], dim=1)
        d4 = self.decoder_block4(d3)
        d4 = torch.cat([x1, d4], dim=1)
        return d4

其中，VGG16_UNET模型包含了一个Encoder和一个Decoder，Encoder中包含了5个卷积层和池化层，Decoder中包含了4个卷积层和反卷积层。在forward函数中，我们首先通过Encoder将输入的图像特征逐步降采样，然后通过Decoder将特征图逐步上采样，最终输出256x256大小的特征图。