pytorch masked fill方法簡單理解

2021-10-12 09:20:42 字數 2084 閱讀 8252

>>> help(torch.tensor.masked_fill)


help on method_descriptor:


masked_fill(...)


masked_fill(mask, value) -> tensor


out-of-place version of :meth:`torch.tensor.masked_fill_`


>>> help(torch.tensor.masked_fill_)


help on method_descriptor:


masked_fill_(...)


masked_fill_(mask, value)


fills elements of :attr:`self` tensor with :attr:`value` where :attr:`mask` is


true. the shape of :attr:`mask` must be


:ref:`broadcastable 


` with the shape of the underlying


tensor.


args:


mask (booltensor): the boolean mask


value (float): the value to fill in with
masked_fill方法有兩個引數，maske和value，mask是乙個pytorch張量（tensor），元素是布林值，value是要填充的值，填充規則是mask中取值為true位置對應於self的相應位置用value填充。

>>> t = torch.randn(3,2)


>>> t


tensor([[-0.9180, -0.4654],


[ 0.9866, -1.3063],


[ 1.8359, 1.1607]])


>>> m = torch.randint(0,2,(3,2))


>>> m


tensor([[0, 1],


[1, 1],


[1, 0]])


>>> m == 0


tensor([[ true, false],


[false, false],


[false, true]])


>>> t.masked_fill(m == 0, -1e9)


tensor([[-1.0000e+09, -4.6544e-01],


[ 9.8660e-01, -1.3063e+00],


[ 1.8359e+00, -1.0000e+09]])
注意：引數m必須與t的size相同或者兩者是可廣播(broadcasting-semantics)的 如下

>>> m = torch.randint(0, 2, (3, 1))


>>> m


tensor([[0],


[1],


[1]])


# m和t是可廣播的


>>> t.masked_fill(m == 0, -1e9)


tensor([[-1.0000e+09, -1.0000e+09],


[ 9.8660e-01, -1.3063e+00],


[ 1.8359e+00, 1.1607e+00]])


>>> m = torch.randint(0, 2, (3, 3))


>>> m


tensor([[0, 0, 0],


[1, 1, 0],


[1, 1, 1]])


# m和t是不可廣播的


>>> t.masked_fill(m == 0, -1e9)


traceback (most recent call last):


file "", line 1, in runtimeerror: the size of tensor a (3) must match the size of tensor b (2) at non-singleton dimension 1
關於廣播(broadcasting-semantics)，可參考pytorch 廣播語義(broadcasting semantics)

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