torch bceloss nan

当数据为空时，loss会为nan

import torcha = torch.FloatTensor([])
b = torch.FloatTensor([])loss_fn = torch.nn.BCELoss() # reduce=False, size_average=False)if a.size(0)>0:
x = loss_fn(a, b).item()
print(x)

这个loss为0

    import torcha = torch.FloatTensor([])b = torch.FloatTensor([])loss_fn = torch.nn.BCELoss(reduction='sum')  # reduce=False, size_average=False)# if a.size(0) > 0:x = loss_fn(a, b)print(x.item())

这个loss也为nan

   import torcha = torch.FloatTensor([]).cuda()b = torch.FloatTensor([]).cuda()loss_fn = torch.nn.BCEWithLogitsLoss().cuda()  # reduce=False, size_average=False)# if a.size(0) > 0:x = loss_fn(a, b)print(x.item())

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