masked_select¶

Documentation¶

treetensor.torch.masked_select(input, mask, *args, reduce=None, **kwargs)[source]¶

Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which is a BoolTensor.

Examples:

>>> import torch
>>> import treetensor.torch as ttorch
>>> t = torch.randn(3, 4)
>>> t
tensor([[ 0.0481,  0.1741,  0.9820, -0.6354],
        [ 0.8108, -0.7126,  0.1329,  1.0868],
        [-1.8267,  1.3676, -1.4490, -2.0224]])
>>> ttorch.masked_select(t, t > 0.3)
tensor([0.9820, 0.8108, 1.0868, 1.3676])

>>> tt = ttorch.randn({
...     'a': (2, 3),
...     'b': {'x': (3, 4)},
... })
>>> tt
<Tensor 0x7f0be77bbc88>
├── a --> tensor([[ 1.1799,  0.4652, -1.7895],
│                 [ 0.0423,  1.0866,  1.3533]])
└── b --> <Tensor 0x7f0be77bbb70>
    └── x --> tensor([[ 0.8139, -0.6732,  0.0065,  0.9073],
                      [ 0.0596, -2.0621, -0.1598, -1.0793],
                      [-0.0496,  2.1392,  0.6403,  0.4041]])
>>> ttorch.masked_select(tt, tt > 0.3)
tensor([1.1799, 0.4652, 1.0866, 1.3533, 0.8139, 0.9073, 2.1392, 0.6403, 0.4041])
>>> ttorch.masked_select(tt, tt > 0.3, reduce=False)
<Tensor 0x7fcb64456b38>
├── a --> tensor([1.1799, 0.4652, 1.0866, 1.3533])
└── b --> <Tensor 0x7fcb64456a58>
    └── x --> tensor([0.8139, 0.9073, 2.1392, 0.6403, 0.4041])

Torch Version Related

This documentation is based on torch.masked_select 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.masked_select(input, mask, *, out=None) → Tensor ¶

Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which is a BoolTensor.

The shapes of the mask tensor and the input tensor don’t need to match, but they must be broadcastable.

Note

The returned tensor does not use the same storage as the original tensor

Args:: input (Tensor): the input tensor. mask (BoolTensor): the tensor containing the binary mask to index with
Keyword args:: out (Tensor, optional): the output tensor.

Example:

>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.3552, -2.3825, -0.8297,  0.3477],
        [-1.2035,  1.2252,  0.5002,  0.6248],
        [ 0.1307, -2.0608,  0.1244,  2.0139]])
>>> mask = x.ge(0.5)
>>> mask
tensor([[False, False, False, False],
        [False, True, True, True],
        [False, False, False, True]])
>>> torch.masked_select(x, mask)
tensor([ 1.2252,  0.5002,  0.6248,  2.0139])