PYTHON写一段UNET分割代码，数据集图像在data/train/trainvol，标签在data/train/trainseg,验证集图像在valvol,标签在valseg

以下是一个简单的基于PyTorch的UNET分割代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
from torch.utils.data import Dataset

import os
from PIL import Image

class CustomDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.images = os.listdir(os.path.join(root_dir, 'trainvol'))
        self.masks = os.listdir(os.path.join(root_dir, 'trainseg'))
        
    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        img_path = os.path.join(self.root_dir, 'trainvol', self.images[idx])
        mask_path = os.path.join(self.root_dir, 'trainseg', self.masks[idx])
        image = Image.open(img_path).convert('RGB')
        mask = Image.open(mask_path).convert('L')

        if self.transform:
            image = self.transform(image)
            mask = self.transform(mask)

        return image, mask

class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()

        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
        self.pool1 = nn.MaxPool2d(2, 2)

        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv4 = nn.Conv2d(128, 128, 3, padding=1)
        self.pool2 = nn.MaxPool2d(2, 2)

        self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv6 = nn.Conv2d(256, 256, 3, padding=1)
        self.pool3 = nn.MaxPool2d(2, 2)

        self.conv7 = nn.Conv2d(256, 512, 3, padding=1)
        self.conv8 = nn.Conv2d(512, 512, 3, padding=1)
        self.up1 = nn.ConvTranspose2d(512, 256, 2, stride=2)

        self.conv9 = nn.Conv2d(512, 256, 3, padding=1)
        self.conv10 = nn.Conv2d(256, 256, 3, padding=1)
        self.up2 = nn.ConvTranspose2d(256, 128, 2, stride=2)

        self.conv11 = nn.Conv2d(256, 128, 3, padding=1)
        self.conv12 = nn.Conv2d(128, 128, 3, padding=1)
        self.up3 = nn.ConvTranspose2d(128, 64, 2, stride=2)

        self.conv13 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv14 = nn.Conv2d(64, 64, 3, padding=1)
        self.conv15 = nn.Conv2d(64, 2, 1)

    def forward(self, x):
        c1 = F.relu(self.conv1(x))
        c1 = F.relu(self.conv2(c1))
        p1 = self.pool1(c1)

        c2 = F.relu(self.conv3(p1))
        c2 = F.relu(self.conv4(c2))
        p2 = self.pool2(c2)

        c3 = F.relu(self.conv5(p2))
        c3 = F.relu(self.conv6(c3))
        p3 = self.pool3(c3)

        c4 = F.relu(self.conv7(p3))
        c4 = F.relu(self.conv8(c4))

        u1 = self.up1(c4)
        u1 = torch.cat([u1, c3], dim=1)
        c5 = F.relu(self.conv9(u1))
        c5 = F.relu(self.conv10(c5))

        u2 = self.up2(c5)
        u2 = torch.cat([u2, c2], dim=1)
        c6 = F.relu(self.conv11(u2))
        c6 = F.relu(self.conv12(c6))

        u3 = self.up3(c6)
        u3 = torch.cat([u3, c1], dim=1)
        c7 = F.relu(self.conv13(u3))
        c7 = F.relu(self.conv14(c7))

        out = self.conv15(c7)

        return out

def train_net(net, device, train_loader, optimizer, criterion):
    net.train()

    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = net(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

def val_net(net, device, val_loader, criterion):
    net.eval()
    val_loss = 0
    with torch.no_grad():
        for data, target in val_loader:
            data, target = data.to(device), target.to(device)
            output = net(data)
            val_loss += criterion(output, target)
    return val_loss / len(val_loader)

def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    transformation = transforms.Compose([transforms.Resize((256, 256)),
                                          transforms.ToTensor()])

    train_dataset = CustomDataset("data/train", transform=transformation)
    val_dataset = CustomDataset("data/val", transform=transformation)

    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
    val_loader = DataLoader(val_dataset, batch_size=16, shuffle=True)

    net = UNet().to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(), lr=0.01)

    for epoch in range(10):
        train_net(net, device, train_loader, optimizer, criterion)
        val_loss = val_net(net, device, val_loader, criterion)
        print("Epoch: {}, Validation Loss: {}".format(epoch+1, val_loss))

if __name__ == '__main__':
    main()

请确保将数据集文件夹放在与代码文件相同的目录中，并按以下方式组织文件夹：

data/
    train/
        trainvol/
            image_1.png
            image_2.png
            ...
        trainseg/
            mask_1.png
            mask_2.png
            ...
    val/
        valvol/
            image_1.png
            image_2.png
            ...
        valseg/
            mask_1.png
            mask_2.png
            ...

注意，此代码仅用于示例目的，可能需要根据您的数据集进行更改和调整。