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 v1.10.0+cu102. 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.
1 | python -c 'import torch;print(torch.__version__)' |
The arguments and keyword arguments supported in torch v1.10.0+cu102 is listed below.
Description From Torch v1.10.0+cu102¶
-
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. Negativedim
will correspond tounsqueeze()
applied atdim
=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]])