pytorch 和 numpy 中 flatten() 用法说明

1. $ndarray.flatten()$

1.1 $ndarray.flatten()$

• 适用于numpy对象
  flatten是numpy.ndarray.flatten的一个函数，即返回一个折叠成一维的数组。

Parameters:
ndarray.flatten(order=‘C’) Return a copy of the array collapsed into one dimension. order : {‘C’, ‘F’, ‘A’, ‘K’}, optional：

• ‘C’ means to flatten in row-major (C-style) order.
• ‘F’ means to flatten in column-major (Fortran- style) order.
• ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise.
• ‘K’ means to flatten a in the order the elements occur in memory.
  The default is ‘C’.

• 举例：
  参数是 str , ‘C’: 按行展开， ’F‘： 按列展开

>>> import numpy as np
>>> a = np.array([[1,2,3],[4,5,6]])
>>> a
array([[1, 2, 3],
       [4, 5, 6]])
>>> a.flatten('C')
array([1, 2, 3, 4, 5, 6])
>>> a.flatten('F')
array([1, 4, 2, 5, 3, 6])

• list 展开：
  对list，使用列表表达式。

Python中flatten( ),matrix.A用法说明

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1.2 $ndarray.flatten()$ 和 $ndarray.ravel()$

$ndarray.flatten()$ 和 $ndarray.ravel()$ 都是对向量的展平操作，区别在于：

• $ndarray.flatten()$ ： 返回原数组副本
• $ndarray.ravel()$： 不返回原数组副本

>>> a.flatten()[0] = -1
>>> a
array([[1, 2, 3],
       [4, 5, 6]])
>>> a.ravel()[0] = -1
>>> a
array([[-1,  2,  3],
       [ 4,  5,  6]])

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1.3 $ndarray.squeeze()$

$ndarray.squeeze(dim=a)$： 对维数为1的维数降维,， 可以指定维数

>>> b = np.array([[[1],[2]]])
>>> b
array([[[1],
        [2]]])
>>> b.shape
(1, 2, 1)
>>> b.squeeze()
array([1, 2])
>>> b.squeeze(0)
array([[1],
       [2]])
>>> b.squeeze(2)
array([[1, 2]])

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2. $torch.flatten()$

2.1 $torch.flatten()$

$torch.flatten(input,star{t}_{d}im=0,en{d}_{d}im=-1)\to Tensor$

可以指定开始、结束展开的维度。
如从 dim = 1 开始, shape: (2, 2, 2) -> (2, 4)

>>> t = torch.tensor([[[1, 2],
...                    [3, 4]],
...                   [[5, 6],
...                    [7, 8]]])
>>> torch.flatten(t)
tensor([1, 2, 3, 4, 5, 6, 7, 8])
>>> torch.flatten(t, start_dim=1)
tensor([[1, 2, 3, 4],
        [5, 6, 7, 8]])

TORCH.FLATTEN

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2.2 $torch.squeeze()$ 和 $torch.unsqueeze()$

torch 中也提供 squeeze 函数， 同 numpy
torch.squeeze(input, dim=None) → Tensor

>>> x = torch.zeros(2, 1, 2, 1, 2)
>>> x.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x)
>>> y.size()
torch.Size([2, 2, 2])
>>> y = torch.squeeze(x, 0)
>>> y.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x, 1)
>>> y.size()
torch.Size([2, 2, 1, 2])

此外， torch 中还提供了 torch.unsqueeze，扩充维度。
$torch.unsqueeze(input,dim)\to Tensor$

>>> x = torch.tensor([1, 2, 3, 4])
>>> torch.unsqueeze(x, 0)
tensor([[ 1,  2,  3,  4]])
>>> torch.unsqueeze(x, 1)
tensor([[ 1],
        [ 2],
        [ 3],
        [ 4]])