pytorch写一个图像处理相关的DNN学习程序

以下是一个基本的图像分类DNN程序，使用PyTorch实现：

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

# 数据预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 加载训练集和测试集
trainset = torchvision.datasets.ImageFolder(root='train/', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
testset = torchvision.datasets.ImageFolder(root='test/', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)

# 定义模型
class Net(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = torch.nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = torch.nn.Linear(128 * 56 * 56, 512)
        self.fc2 = torch.nn.Linear(512, 2)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 128 * 56 * 56)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = Net()

# 定义损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy: %d %%' % (100 * correct / total))

在这个程序中，我们首先对图像进行了一些预处理，包括缩放、剪裁、转换为张量和标准化。然后我们定义了一个包含两个卷积层和两个全连接层的神经网络模型。我们使用交叉熵损失函数和随机梯度下降优化器进行训练。在训练期间，我们使用批量梯度下降来更新模型参数。最后，我们进行了模型测试，并计算了分类准确率