full_like¶
Documentation¶
-
treetensor.torch.
full_like
(input, *args, **kwargs)[source]¶ In
treetensor
, you can useones_like
to create a tree of tensors with all the same value of like another tree.Example:
>>> import torch >>> import treetensor.torch as ttorch >>> ttorch.full_like(torch.randn(2, 3), 2.3) # the same as torch.full_like(torch.randn(2, 3), 2.3) tensor([[2.3000, 2.3000, 2.3000], [2.3000, 2.3000, 2.3000]]) >>> ttorch.full_like({ ... 'a': torch.randn(2, 3), ... 'b': {'x': torch.randn(4, )}, ... }, 2.3) <Tensor 0x7ff363bb6cf8> ├── a --> tensor([[2.3000, 2.3000, 2.3000], │ [2.3000, 2.3000, 2.3000]]) └── b --> <Tensor 0x7ff363bb69e8> └── x --> tensor([2.3000, 2.3000, 2.3000, 2.3000])
Torch Version Related
This documentation is based on torch.full_like 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.
full_like
(input, fill_value, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) → Tensor¶ Returns a tensor with the same size as
input
filled withfill_value
.torch.full_like(input, fill_value)
is equivalent totorch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)
.- Args:
input (Tensor): the size of
input
will determine size of the output tensor. fill_value: the number to fill the output tensor with.- Keyword args:
- dtype (
torch.dtype
, optional): the desired data type of returned Tensor. Default: if
None
, defaults to the dtype ofinput
.- layout (
torch.layout
, optional): the desired layout of returned tensor. Default: if
None
, defaults to the layout ofinput
.- device (
torch.device
, optional): the desired device of returned tensor. Default: if
None
, defaults to the device ofinput
.- requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default:
False
.- memory_format (
torch.memory_format
, optional): the desired memory format of returned Tensor. Default:
torch.preserve_format
.
- dtype (