torch.squeeze() - torch.squeeze_() - v1.5.0

旧城等待， 2023-02-17 14:55 19阅读 0赞

#  torch.squeeze() - torch.squeeze\_() - v1.5.0 #

*  `torch.squeeze` (Python function, in `torch`)
 *  `torch.Tensor.squeeze` (Python method, in `torch.Tensor`)
 *  `torch.Tensor.squeeze_` (Python method, in `torch.Tensor`)

**torch.Tensor**  
[https://pytorch.org/docs/stable/tensors.html][https_pytorch.org_docs_stable_tensors.html]

`squeeze(dim=None)` \-> Tensor  
See `torch.squeeze()`

`squeeze_(dim=None)` \-> Tensor  
In-place version of `squeeze()`

**torch**  
[https://pytorch.org/docs/stable/torch.html][https_pytorch.org_docs_stable_torch.html]

`torch.squeeze(input, dim=None, out=None)` \-> Tensor  
Returns a tensor with all the dimensions of input of size 1 removed.  
将输入张量形状中的 1 去除并返回。

缩减维度。

For example, if input is of shape:  ( A × 1 × B × C × 1 × D ) (A \\times 1 \\times B \\times C \\times 1 \\times D) (A×1×B×C×1×D) then the out tensor will be of shape:  ( A × B × C × D ) (A \\times B \\times C \\times D) (A×B×C×D).  
如果输入是形状  ( A × 1 × B × C × 1 × D ) (A \\times 1 \\times B \\times C \\times 1 \\times D) (A×1×B×C×1×D)，那么输出形状就为： ( A × B × C × D ) (A \\times B \\times C \\times D) (A×B×C×D)。

When `dim` is given, a squeeze operation is done only in the given dimension. If input is of shape:  ( A × 1 × B ) (A \\times 1 \\times B) (A×1×B), `squeeze(input, 0)` leaves the tensor unchanged, but `squeeze(input, 1)` will squeeze the tensor to the shape  ( A × B ) (A \\times B) (A×B).  
当给定 `dim` 时，那么挤压操作只在给定维度上。例如，输入形状为： ( A × 1 × B ) (A \\times 1 \\times B) (A×1×B), `squeeze(input, 0)` 将会保持张量不变，只有用 `squeeze(input, 1)`，形状会变成  ( A × B ) (A \\times B) (A×B)。

**The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.**  
返回张量与输入张量共享内存，所以改变其中一个的内容会改变另一个。

## 1. Parameters ##

`input (Tensor)` – the input tensor. (输入张量。)

`dim (int, optional)` – if given, the input will be squeezed only in this dimension. (如果给定，则 input 只会在给定维度挤压。)

`out (Tensor, optional)` – the output tensor. (输出张量。)

## 2. Example ##

### 2.1 example ###

(pt-1.4_py-3.6) yongqiang@yongqiang:~$ python
    Python 3.6.10 |Anaconda, Inc.| (default, May  8 2020, 02:54:21)
    [GCC 7.3.0] on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>> import torch
    >>> data = torch.zeros(2, 1, 2, 1, 2)
    >>> data
    tensor([[[[[0., 0.]],
    
              [[0., 0.]]]],
    
    
    
            [[[[0., 0.]],
    
              [[0., 0.]]]]])
    >>> data.size()
    torch.Size([2, 1, 2, 1, 2])
    >>>
    >>> data_squ = torch.squeeze(data)
    >>> data_squ
    tensor([[[0., 0.],
             [0., 0.]],
    
            [[0., 0.],
             [0., 0.]]])
    >>> data_squ.size()
    torch.Size([2, 2, 2])
    >>>
    >>> data_squ0 = torch.squeeze(data, 0)
    >>> data_squ0
    tensor([[[[[0., 0.]],
    
              [[0., 0.]]]],
    
    
    
            [[[[0., 0.]],
    
              [[0., 0.]]]]])
    >>> data_squ0.size()
    torch.Size([2, 1, 2, 1, 2])
    >>>
    >>> data_squ1 = torch.squeeze(data, 1)
    >>> data_squ1
    tensor([[[[0., 0.]],
    
             [[0., 0.]]],
    
    
            [[[0., 0.]],
    
             [[0., 0.]]]])
    >>> data_squ1.size()
    torch.Size([2, 2, 1, 2])
    >>>
    >>> exit()
    (pt-1.4_py-3.6) yongqiang@yongqiang:~$

[https_pytorch.org_docs_stable_tensors.html]: https://pytorch.org/docs/stable/tensors.html
[https_pytorch.org_docs_stable_torch.html]: https://pytorch.org/docs/stable/torch.html