长荣笔试(07.24)程序

本文通过一个C++程序示例介绍了类的多继承及其构造和析构过程。示例中定义了四个类:A、B、C 和 D,其中 D 类继承自 A、B 和 C,并展示了构造函数和析构函数的调用顺序。

写出下列程序输出的结果

 

#include <iostream>

using namespace std;

class A
{
public:
    A(){cout<< "A Begin" << endl;}
 ~A(){cout << "A End" << endl;}
};

class B
{
public:
    B(){cout << "B Begin" << endl;}
 ~B(){cout << "B End" << endl;}
};

class C
{
public:
    C(){cout << "C Begin" << endl;}
 ~C(){cout << "C End" << endl;}
};

class D: public A, B, C
{
public:
  
 D(){
  A();
  cout << "D Begin" << endl;
  }
 ~D(){cout << "D End" << endl;}
};

int main()
{
  D d;
 return 0;
}

C:\Users\adminstor\anaconda3\envs\python39\python.exe D:\daima\KalmanNet_TSP-main\main_lor_DT_NLobs.py Pipeline Start Current Time = 07.24.23_12:19:44 Using GPU 1/r2 [dB]: tensor(30.) 1/q2 [dB]: tensor(30.) Start Data Gen Data Load data_lor_v0_rq3030_T20.pt no chopping trainset size: torch.Size([1000, 3, 20]) cvset size: torch.Size([100, 3, 20]) testset size: torch.Size([200, 3, 20]) Evaluate EKF full Extended Kalman Filter - MSE LOSS: tensor(-26.4659) [dB] Extended Kalman Filter - STD: tensor(1.6740) [dB] Inference Time: 37.115127086639404 KalmanNet start Number of trainable parameters for KNet: 19938 Composition Loss: True Traceback (most recent call last): File "D:\daima\KalmanNet_TSP-main\main_lor_DT_NLobs.py", line 146, in <module> [MSE_cv_linear_epoch, MSE_cv_dB_epoch, MSE_train_linear_epoch, MSE_train_dB_epoch] = KalmanNet_Pipeline.NNTrain(sys_model, cv_input, cv_target, train_input, train_target, path_results) File "D:\daima\KalmanNet_TSP-main\Pipelines\Pipeline_EKF.py", line 150, in NNTrain MSE_trainbatch_linear_LOSS = self.alpha * self.loss_fn(x_out_training_batch, train_target_batch)+(1-self.alpha)*self.loss_fn(y_hat, y_training_batch) File "C:\Users\adminstor\anaconda3\envs\python39\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "C:\Users\adminstor\anaconda3\envs\python39\lib\site-packages\torch\nn\modules\loss.py", line 520, in forward return F.mse_loss(input, target, reduction=self.reduction) File "C:\Users\adminstor\anaconda3\envs\python39\lib\site-packages\torch\nn\functional.py", line 3112, in mse_loss return torch._C._nn.mse_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction)) RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
07-25
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