给我一份在pytorch框架下resnet34_unet的代码，输出特征图维度为3

好的，以下是在Pytorch框架下实现ResNet34_UNet网络的示例代码，输出特征图维度为3：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models


class ResNet34_UNet(nn.Module):
    def __init__(self, num_classes=3):
        super(ResNet34_UNet, self).__init__()

        # Encoder (ResNet34)
        self.encoder = models.resnet34(pretrained=True)
        self.relu = nn.ReLU(inplace=True)

        # Decoder (UNet)
        self.upconv1 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.conv1 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(256)

        self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(128)

        self.upconv3 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(64)

        self.upconv4 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2)
        self.conv4 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(32)

        self.conv5 = nn.Conv2d(32, num_classes, kernel_size=1)

    def forward(self, x):
        # Encoder (ResNet34)
        x1 = self.encoder.conv1(x)
        x1 = self.encoder.bn1(x1)
        x1 = self.relu(x1)
        x1 = self.encoder.maxpool(x1)

        x2 = self.encoder.layer1(x1)
        x3 = self.encoder.layer2(x2)
        x4 = self.encoder.layer3(x3)
        x5 = self.encoder.layer4(x4)

        # Decoder (UNet)
        x = self.upconv1(x5)
        x = torch.cat([x, x4], dim=1)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.upconv2(x)
        x = torch.cat([x, x3], dim=1)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        x = self.upconv3(x)
        x = torch.cat([x, x2], dim=1)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)

        x = self.upconv4(x)
        x = torch.cat([x, x1], dim=1)
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu(x)

        x = self.conv5(x)

        return x

其中，`num_classes` 表示输出的特征图的通道数，这里设置为3。如果需要调整输出特征图的通道数，只需修改 `num_classes` 的值即可。