clamp

Documentation

treetensor.torch.clamp(input, *args, **kwargs)

Clamp all elements in input into the range [ min, max ].

Examples:

>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.clamp(ttorch.tensor([-1.7120,  0.1734, -0.0478, 2.0922]), min=-0.5, max=0.5)
tensor([-0.5000,  0.1734, -0.0478,  0.5000])

>>> ttorch.clamp(ttorch.tensor({
...     'a': [-1.7120,  0.1734, -0.0478, 2.0922],
...     'b': {'x': [[-0.9049, 1.7029, -0.3697], [0.0489, -1.3127, -1.0221]]},
... }), min=-0.5, max=0.5)
<Tensor 0x7fbf5332a7c0>
├── a --> tensor([-0.5000,  0.1734, -0.0478,  0.5000])
└── b --> <Tensor 0x7fbf5332a880>
    └── x --> tensor([[-0.5000,  0.5000, -0.3697],
                      [ 0.0489, -0.5000, -0.5000]])

Torch Version Related

This documentation is based on torch.clamp in torch v1.9.0+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.

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

The arguments and keyword arguments supported in torch v1.9.0+cu102 is listed below.

Description From Torch v1.9.0+cu102

torch.clamp(input, min=None, max=None, *, out=None) → Tensor

Clamps all elements in input into the range [ min, max ]. Letting min_value and max_value be min and max, respectively, this returns:

\[y_i = \min(\max(x_i, \text{min\_value}_i), \text{max\_value}_i)\]

If min is None, there is no lower bound. Or, if max is None there is no upper bound.

Note

If min is greater than max torch.clamp(..., min, max) sets all elements in input to the value of max.

Args:

input (Tensor): the input tensor. min (Number or Tensor, optional): lower-bound of the range to be clamped to max (Number or Tensor, optional): upper-bound of the range to be clamped to

Keyword args:

out (Tensor, optional): the output tensor.

Example:

>>> a = torch.randn(4)
>>> a
tensor([-1.7120,  0.1734, -0.0478, -0.0922])
>>> torch.clamp(a, min=-0.5, max=0.5)
tensor([-0.5000,  0.1734, -0.0478, -0.0922])

>>> min = torch.linspace(-1, 1, steps=4)
>>> torch.clamp(a, min=min)
tensor([-1.0000,  0.1734,  0.3333,  1.0000])