uNITY string 赚转枚举

初始化模型和优化器
model = ResNet(BasicBlock, [2, 2, 2, 2]).to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion = nn.CrossEntropyLoss在Unity中，可以使用字符串来表示枚举的值，并且可以通过转换函数将字符串转换为枚举类型。()

# 训练模型
def train(model, train_loader, optimizer, criterion):
model.train()

for images, labels以下是一个示例代码：

public enum MyEnum
{
    Option1,
    Option2,
    Option3
 in train_loader:
        images = images.to(device)
        labels = labels.to(device)
        
        optimizer.zero_grad()
        
        outputs =}

public static class EnumConverter
{
    public static MyEnum StringToEnum(string value)
    {
        return (MyEnum)System.Enum model(images)
        loss = criterion(outputs, labels)
        
        loss.backward()
        optimizer.step()

def test(model, test_loader, criterion.Parse(typeof(MyEnum), value);
    }

    public static string EnumToString(MyEnum value)
    {
        return value.ToString();
    }
}

):
    model.eval()
    test_loss = 0
    correct = 0
    
    with torch.no_grad():
        for images, labels// 使用示例
string enumString = "Option2";
MyEnum enumValue = EnumConverter.StringToEnum(enumString);
Debug.Log(enum in test_loader:
            images = images.to(device)
            labels = labels.to(device)
            
            outputs = model(images)
            loss =Value); // 输出：Option2

MyEnum myEnum = MyEnum.Option3;
string myEnumString = EnumConverter.EnumToString(myEnum criterion(outputs, labels)
            test_loss += loss.item()
            
            _, predicted = outputs.max(1)
            correct += predicted.eq(labels);
Debug.Log(myEnumString); // 输出：Option3

在上面的示例中，`MyEnum`是一个).sum().item()

test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset简单的枚举类型。`EnumConverter`类提供了两个静态方法，`StringToEnum`用于将)

print(f"Test Loss: {test_loss:.4f}, Accuracy: {accuracy:.2f}%")

# 开始训字符串转换为枚举值，`EnumToString`用于将枚举值转换为字符串。你可以根据自己的需求修改这些代码来适应你的具体情况。