转自：https://discuss.pytorch.org/t/why-do-we-need-to-do-loss-cuda-when-we-we-have-already-done-model-cuda/91023/5

https://discuss.pytorch.org/t/move-the-loss-function-to-gpu/20060

1.问题

有的模型将损失函数也调用了cuda():

 if torch.cuda.is_available():
        net.cuda()
        softMax.cuda()
        CE_loss.cuda()
        Dice_loss.cuda()

但如果损失函数没有参数，且输入的tensor本身就在cuda上面，就没有必要调用.cuda()。

需要调用的情况：

output = torch.randn(10, 10, requires_grad=True, device='cuda')
target = torch.randint(0, 10, (10,), device='cuda')

weight = torch.empty(10).uniform_(0, 1)
criterion = nn.CrossEntropyLoss(weight=weight)

loss = criterion(output, target) # error
> RuntimeError: Expected object of device type cuda but got device type cpu for argument 
#3 'weight' in call to _thnn_nll_loss_forward

criterion.cuda()
loss = criterion(output, target) # works

因为损失函数中用到了weight权重计算，所以需要把criterion 也放到cuda上。

标签：loss,weight,10,self,torch,criterion,cuda
来源： https://www.cnblogs.com/BlueBlueSea/p/15542320.html