For the CrossEntropyLoss 47 you feed a score vector of batch x classes (to be converted by softmax to probabilities p of batch x classes, i.e. all non-negative, sum over each row is one) and a target class label.
For the BCELoss 62 you supply a probability p of class 1 (with 1-p being that of class 0) and a class label 0/1 (or a probability, too, if you wanted).
So this will give the same number twice:
score = Variable(torch.randn(10,2))
target = Variable((torch.rand(10)>0.5).long())
lfn1 = torch.nn.CrossEntropyLoss()
lfn2 = torch.nn.BCELoss()
print(lfn1(score,target), lfn2(torch.nn.functional.softmax(score)[:,1],target.float()))

Best regards
Thomas