import torch

from torch import nn

class MseDirectionLoss(nn.Module):

def **init**(self, lamda):

super(MseDirectionLoss, self).**init**()

self.lamda = lamda

self.criterion = nn.MSELoss()

self.similarity_loss = torch.nn.CosineSimilarity()

```
def forward(self, output_pred, output_real):
y_pred_0, y_pred_1, y_pred_2, y_pred_3 = output_pred[3], output_pred[6], output_pred[9], output_pred[12]
y_0, y_1, y_2, y_3 = output_real[3], output_real[6], output_real[9], output_real[12]
# different terms of loss
abs_loss_0 = self.criterion(y_pred_0, y_0)
loss_0 = torch.mean(1 - self.similarity_loss(y_pred_0.view(y_pred_0.shape[0], -1), y_0.view(y_0.shape[0], -1)))
abs_loss_1 = self.criterion(y_pred_1, y_1)
loss_1 = torch.mean(1 - self.similarity_loss(y_pred_1.view(y_pred_1.shape[0], -1), y_1.view(y_1.shape[0], -1)))
abs_loss_2 = self.criterion(y_pred_2, y_2)
loss_2 = torch.mean(1 - self.similarity_loss(y_pred_2.view(y_pred_2.shape[0], -1), y_2.view(y_2.shape[0], -1)))
abs_loss_3 = self.criterion(y_pred_3, y_3)
loss_3 = torch.mean(1 - self.similarity_loss(y_pred_3.view(y_pred_3.shape[0], -1), y_3.view(y_3.shape[0], -1)))
total_loss = loss_0 + loss_1 + loss_2 + loss_3 + self.lamda * (
abs_loss_0 + abs_loss_1 + abs_loss_2 + abs_loss_3)
return total_loss
```

class DirectionOnlyLoss(nn.Module):

def **init**(self):

super(DirectionOnlyLoss, self).**init**()

self.similarity_loss = torch.nn.CosineSimilarity()

```
def forward(self, output_pred, output_real):
y_pred_0, y_pred_1, y_pred_2, y_pred_3 = output_pred[3], output_pred[6], output_pred[9], output_pred[12]
y_0, y_1, y_2, y_3 = output_real[3], output_real[6], output_real[9], output_real[12]
loss_0 = torch.mean(1 - self.similarity_loss(y_pred_0.view(y_pred_0.shape[0], -1), y_0.view(y_0.shape[0], -1)))
loss_1 = torch.mean(1 - self.similarity_loss(y_pred_1.view(y_pred_1.shape[0], -1), y_1.view(y_1.shape[0], -1)))
loss_2 = torch.mean(1 - self.similarity_loss(y_pred_2.view(y_pred_2.shape[0], -1), y_2.view(y_2.shape[0], -1)))
loss_3 = torch.mean(1 - self.similarity_loss(y_pred_3.view(y_pred_3.shape[0], -1), y_3.view(y_3.shape[0], -1)))
total_loss = loss_0 + loss_1 + loss_2 + loss_3
return total_loss
```

error is：