randint¶
Documentation¶
-
treetensor.torch.
randint
(*args, **kwargs)[source]¶ In
treetensor
, you can userandint
to create a tree of tensors with numbers in an integer range.Examples:
>>> import torch >>> import treetensor.torch as ttorch >>> ttorch.randint(10, (2, 3)) # the same as torch.randint(10, (2, 3)) tensor([[3, 4, 5], [4, 5, 5]]) >>> ttorch.randint(10, {'a': (2, 3), 'b': {'x': (4, )}}) <Tensor 0x7ff363bb6438> ├── a --> tensor([[5, 3, 7], │ [8, 1, 8]]) └── b --> <Tensor 0x7ff363bb6240> └── x --> tensor([8, 8, 2, 4])
Torch Version Related
This documentation is based on torch.randint 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.
randint
(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor¶ Returns a tensor filled with random integers generated uniformly between
low
(inclusive) andhigh
(exclusive).The shape of the tensor is defined by the variable argument
size
.Note
With the global dtype default (
torch.float32
), this function returns a tensor with dtypetorch.int64
.- Args:
low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. high (int): One above the highest integer to be drawn from the distribution. size (tuple): a tuple defining the shape of the output tensor.
- Keyword args:
generator (
torch.Generator
, optional): a pseudorandom number generator for sampling out (Tensor, optional): the output tensor. dtype (torch.dtype, optional) - the desired data type of returned tensor. Default: ifNone
,this function returns a tensor with dtype
torch.int64
.- layout (
torch.layout
, optional): the desired layout of returned Tensor. Default:
torch.strided
.- device (
torch.device
, optional): the desired device of returned tensor. Default: if
None
, uses the current device for the default tensor type (seetorch.set_default_tensor_type()
).device
will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.- requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default:
False
.
- layout (
Example:
>>> torch.randint(3, 5, (3,)) tensor([4, 3, 4]) >>> torch.randint(10, (2, 2)) tensor([[0, 2], [5, 5]]) >>> torch.randint(3, 10, (2, 2)) tensor([[4, 5], [6, 7]])