请用pytorch写一个深度学习半监督语义分割的代码框架

当然，我会提供一个代码框架，并且根据您的数据集和模型选择进行适当的调整。下面是一个简单的深度学习半监督语义分割的代码框架：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from skimage.io import imread
from skimage.transform import resize

# Define hyperparameters
num_epochs = 50
batch_size = 4
learning_rate = 0.001
img_height = 256
img_width = 256
num_classes = 2

# Define transforms for data augmentation
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5), (0.5))
])

# Define dataset class
class SegmentationDataset(Dataset):
    def __init__(self, image_dir, mask_dir=None):
        self.image_dir = image_dir
        self.mask_dir = mask_dir

        self.image_files = sorted(os.listdir(image_dir))

    def __getitem__(self, idx):
        img = imread(self.image_dir + self.image_files[idx])
        img = resize(img, (img_height, img_width))

        if self.mask_dir:
            mask = imread(self.mask_dir + self.image_files[idx], as_gray=True)
            mask = resize(mask, (img_height, img_width))
            mask = (mask > 0).astype(float)
            mask = torch.from_numpy(mask).float()
            return transform(img), mask

        return transform(img)

    def __len__(self):
        return len(self.image_files)

# Define model class
class SegmentationModel(nn.Module):
    def __init__(self):
        super(SegmentationModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(32, num_classes, 1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        return x

# Define training function
def train_model(model, dataloader, criterion, optimizer, device):
    model.train()

    for i, (inputs, targets) in enumerate(dataloader):
        inputs = inputs.to(device)
        targets = targets.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()

        optimizer.step()

# Define testing function
def test_model(model, dataloader, criterion, device):
    model.eval()

    total_loss = 0
    with torch.no_grad():
        for inputs, targets in dataloader:
            inputs = inputs.to(device)
            targets = targets.to(device)

            outputs = model(inputs)
            loss = criterion(outputs, targets)
            total_loss += loss.item()

    return total_loss / len(dataloader)

# Define main function
def main():
    # Define data paths
    image_dir = 'path/to/image/folder/'
    mask_dir = 'path/to/mask/folder/'

    # Split data into train and test sets
    dataset = SegmentationDataset(image_dir, mask_dir)
    train_size = int(0.8 * len(dataset))
    test_size = len(dataset) - train_size
    train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])

    # Define dataloaders for training and testing
    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

    # Instantiate model, criterion, and optimizer
    model = SegmentationModel()
    criterion = nn.BCEWithLogitsLoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    # Move model and criterion to GPU if available
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    criterion.to(device)

    # Train the model
    for epoch in range(num_epochs):
        train_model(model, train_dataloader, criterion, optimizer, device)

        test_loss = test_model(model, test_dataloader, criterion, device)

        print(f'Epoch [{epoch+1}/{num_epochs}], Test Loss: {test_loss:.4f}')

if __name__ == '__main__':
    main()

请注意，这只是一个框架，您需要根据您的数据集和模型调整超参数和网络结构。另外，请不要将此代码或其派生代码用于商业或不当用途。