pytorch 读取文件夹中图片，训练，预测，保存结果代码

以下是一个简单的 PyTorch 读取文件夹中图片、训练、预测和保存结果的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, datasets

# 定义数据集
class ImageDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.data = datasets.ImageFolder(root_dir, transform=transform)

    def __getitem__(self, index):
        return self.data[index]

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

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义训练函数
def train(model, train_loader, criterion, optimizer, device):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(train_loader)

# 定义测试函数
def test(model, test_loader, criterion, device):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    return correct / total

# 定义主函数
def main():
    # 定义超参数
    batch_size = 32
    num_epochs = 10
    learning_rate = 0.001

    # 定义数据增强
    transform = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

    # 加载数据集
    train_dataset = ImageDataset('train', transform=transform)
    test_dataset = ImageDataset('test', transform=transform)
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

    # 定义设备
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # 定义模型、损失函数和优化器
    model = Net().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)

    # 训练模型
    for epoch in range(num_epochs):
        train_loss = train(model, train_loader, criterion, optimizer, device)
        test_acc = test(model, test_loader, criterion, device)
        print('Epoch [{}/{}], Train Loss: {:.4f}, Test Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, test_acc))

    # 保存模型
    torch.save(model.state_dict(), 'model.pth')

    # 预测新数据
    model.eval()
    with torch.no_grad():
        inputs = torch.randn(1, 3, 224, 224).to(device)
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        print('Predicted Class:', predicted.item())

if __name__ == '__main__':
    main()

这段代码可以读取名为 "train" 和 "test" 的文件夹中的图片，使用数据增强进行训练，训练一个简单的卷积神经网络模型，保存模型参数到 "model.pth" 文件中，并使用模型进行预测。