请写出一个简单的基于pytorch的图像去噪代码

好的，下面是一个基于PyTorch的图像去噪代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import Compose, ToTensor, Normalize

# 定义数据处理和增强的transform
transform = Compose([
    ToTensor(),
    Normalize(mean=(0.5,), std=(0.5,))
])

# 加载数据集
train_dataset = ImageFolder('train_data', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 定义模型
class DenoiseNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 256, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 512, 3, padding=1),
            nn.ReLU(),
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 3, 3, stride=2, padding=1, output_padding=1),
            nn.Tanh()
        )
        
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
model = DenoiseNet()
model.train()
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, _ = data
        noisy_inputs = inputs + 0.1 * torch.randn(inputs.size())
        optimizer.zero_grad()
        outputs = model(noisy_inputs)
        loss = criterion(outputs, inputs)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(train_loader)))
    
# 使用模型对图像进行去噪
model.eval()
image = Image.open('test.jpg')
image_tensor = transform(image).unsqueeze(0)
noisy_image_tensor = image_tensor + 0.1 * torch.randn(image_tensor.size())
denoised_image_tensor = model(noisy_image_tensor)
denoised_image = denoised_image_tensor.squeeze().detach().numpy()
denoised_image = (denoised_image + 1) / 2 * 255
denoised_image = denoised_image.astype('uint8')
denoised_image = Image.fromarray(denoised_image)
denoised_image.save('test_denoised.jpg')

这个代码实现了一个简单的自编码器模型，用于对图像进行去噪。模型的输入是带有噪声的图像，输出是去噪后的图像。我们使用均方误差作为损失函数，优化器选择Adam。在训练过程中，我们对输入的图像加入一些随机噪声，以增加模型的鲁棒性。在测试时，我们加载模型并将带噪声的图像输入模型，得到去噪后的图像。最后，我们将得到的图像保存到文件中。