-1 在 pytorch 视图中是什么意思? [英] What does -1 mean in pytorch view?

问题描述

正如问题所说，-1 在 pytorch view 中做了什么?

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) 是否会生成额外的维度?它的行为是否与 numpy reshape -1 相同?

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，即实际值将推断此维度，以便视图中的元素数量与原始元素数量相匹配.

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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