unsqueeze¶

Documentation¶

treetensor.torch.unsqueeze(input, dim)[source]¶

Returns a new tensor with a dimension of size one inserted at the specified position.

Examples:

>>> import torch
>>> import treetensor.torch as ttorch
>>> t1 = torch.randint(100, (100, ))
>>> t1.shape
torch.Size([100])
>>> ttorch.unsqueeze(t1, 0).shape
torch.Size([1, 100])

>>> tt1 = ttorch.randint(100, {
...     'a': (2, 2, 2),
...     'b': {'x': (2, 3)},
... })
>>> tt1.shape
<Size 0x7f5d1a5741d0>
├── a --> torch.Size([2, 2, 2])
└── b --> <Size 0x7f5d1a5740b8>
    └── x --> torch.Size([2, 3])
>>> ttorch.unsqueeze(tt1, 1).shape
<Size 0x7f5d1a5c98d0>
├── a --> torch.Size([2, 1, 2, 2])
└── b --> <Size 0x7f5d1a5c99b0>
    └── x --> torch.Size([2, 1, 3])

Torch Version Related

This documentation is based on torch.unsqueeze in torch v2.0.1+cu117. Its arguments’ arrangements depend on the version of pytorch you installed.

If some arguments listed here are not working properly, please check your pytorch’s version with the following command and find its documentation.

python -c 'import torch;print(torch.__version__)'

The arguments and keyword arguments supported in torch v2.0.1+cu117 is listed below.

Description From Torch v2.0.1+cu117¶

torch.unsqueeze(input, dim) → Tensor ¶

Returns a new tensor with a dimension of size one inserted at the specified position.

The returned tensor shares the same underlying data with this tensor.

A dim value within the range [-input.dim() - 1, input.dim() + 1) can be used. Negative dim will correspond to unsqueeze() applied at dim = dim + input.dim() + 1.

Args:: input (Tensor): the input tensor. dim (int): the index at which to insert the singleton dimension

Example:

>>> 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]])