IDLE SHELL写UNET代码，其中数据集data，子文件为训练集train，验证集val,训练集子文件为图像文件trainvol,标签文件trainseg,验证集子文件为图像文件valvol,标签文件valseg

假设你已经安装好了PyTorch和torchvision，并且已经将数据集文件夹“data”放在了当前目录下，那么你可以按照以下步骤编写UNET代码：

1. 导入必要的库和模块：

import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
from dataset import UNetDataset
from model import UNet

2. 定义数据预处理的transforms：

train_transforms = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(degrees=15),
    transforms.ToTensor()
])

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

3. 创建训练集和验证集的dataset：

train_dataset = UNetDataset(data_dir='data/train',
                            img_dir='trainvol',
                            mask_dir='trainseg',
                            transform=train_transforms)
val_dataset = UNetDataset(data_dir='data/val',
                          img_dir='valvol',
                          mask_dir='valseg',
                          transform=val_transforms)

4. 创建训练集和验证集的dataloader：

train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)

5. 创建UNet模型：

model = UNet()

6. 定义损失函数和优化器：

criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

7. 训练模型：

num_epochs = 10

for epoch in range(num_epochs):
    train_loss = 0.0
    val_loss = 0.0
    
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
    
    model.eval()
    for batch_idx, (data, target) in enumerate(val_loader):
        output = model(data)
        loss = criterion(output, target)
        val_loss += loss.item() * data.size(0)
    
    train_loss /= len(train_dataset)
    val_loss /= len(val_dataset)
    
    print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
        epoch+1, train_loss, val_loss))

8. 至此，你已经完成了UNET模型的训练，可以使用该模型对新的图像进行分割了。