My task is to create a single object detection model, It is doing 2 task: 1. Classification between 2 classes [cat, no object]

2: Regression x and y co_ordinates.

So I want to design a loss function such that it gives me 2 losses for classification and regression each.

for classification I am using CrossEntropy Loss, but for regression I want to calculate the MSE loss only for the images containing cat. So here is a dummy example of such loss function I have created please check if it is right or wrong.

```
output = torch.tensor([[0.7,0.3,0.5,0.6], #(prob_cat, prob_no_object, x,y)
[0.2,0.8,100,100],
[0.8,0.2,0.4,0.5]])
target = torch.tensor([[1,0,0.4,0.8],
[0,1,0,0],
[1,0,0.3,0.4]])
def myloss(output,target):
#classification loss (cat, no object)
target_class = target[:,:2].argmax(1) #True class
output_class = output[:,:2] #Predicted Class
l1 = nn.CrossEntropyLoss(reduction='sum')
Loss1 = l1(output_class,target_class)
#regression loss (x,y)
prob_no_object = target[:,[1]]
prob_object=(prob_no_object==0).float()
target_bbx = target[:,[2,3]]
output_bbx = output[:,[2,3]]
l2 = nn.MSELoss(reduction='none')
L2 = l2(output_bbx,target_bbx)
# print(L2.shape)
Loss2 = torch.sum(torch.mul(prob_object,L2))
return(Loss1,Loss2/torch.sum(prob_object))
#classification loss, regression_loss_per_cat_img_in_batch
```