在pytorch视图中-1是什么意思? [英] What does -1 mean in pytorch view?
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问题描述
正如问题所述,在pytorch view
中 -1
会做什么?
As the question says, what does -1
do in pytorch view
?
>>> a = torch.arange(1, 17)
>>> a
tensor([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.,
11., 12., 13., 14., 15., 16.])
>>> a.view(1,-1)
tensor([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.,
11., 12., 13., 14., 15., 16.]])
>>> a.view(-1,1)
tensor([[ 1.],
[ 2.],
[ 3.],
[ 4.],
[ 5.],
[ 6.],
[ 7.],
[ 8.],
[ 9.],
[ 10.],
[ 11.],
[ 12.],
[ 13.],
[ 14.],
[ 15.],
[ 16.]])
它( -1
)是否会产生额外的尺寸?它的行为与numpy reshape
-1
相同吗?
Does it (-1
) generate additional dimension?
Does it behave the same as numpy reshape
-1
?
推荐答案
是的,它的行为类似于 numpy.reshape()
中的 -1
,即实际值会推断出该尺寸,以便视图中的元素数与原始元素数相匹配.
Yes, it does behave like -1
in numpy.reshape()
, i.e. the actual value for this dimension will be inferred so that the number of elements in the view matches the original number of elements.
例如:
import torch
x = torch.arange(6)
print(x.view(3, -1)) # inferred size will be 2 as 6 / 3 = 2
# tensor([[ 0., 1.],
# [ 2., 3.],
# [ 4., 5.]])
print(x.view(-1, 6)) # inferred size will be 1 as 6 / 6 = 1
# tensor([[ 0., 1., 2., 3., 4., 5.]])
print(x.view(1, -1, 2)) # inferred size will be 3 as 6 / (1 * 2) = 3
# tensor([[[ 0., 1.],
# [ 2., 3.],
# [ 4., 5.]]])
# print(x.view(-1, 5)) # throw error as there's no int N so that 5 * N = 6
# RuntimeError: invalid argument 2: size '[-1 x 5]' is invalid for input with 6 elements
print(x.view(-1, -1, 3)) # throw error as only one dimension can be inferred
# RuntimeError: invalid argument 1: only one dimension can be inferred
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