Quick Start

Create a Tree-based Tensor

You can create a tree-based tensor or a native tensor like the following example code.

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import builtins
import os
from functools import partial

import treetensor.torch as torch

print = partial(builtins.print, sep=os.linesep)

if __name__ == '__main__':
    t1 = torch.tensor([[1, 2, 3],
                       [4, 5, 6]])
    print('new native tensor:', t1)

    t2 = torch.tensor({
        'a': [1, 2, 3],
        'b': {'x': [[4, 5], [6, 7]]},
    })
    print('new tree tensor:', t2)

    t3 = torch.randn(2, 3)
    print('new random native tensor:', t3)

    t4 = torch.randn({
        'a': (2, 3),
        'b': {'x': (3, 4)},
    })
    print('new random tree tensor:', t4)

The output should be like below.

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new native tensor:
tensor([[1, 2, 3],
        [4, 5, 6]])
new tree tensor:
<Tensor 0x7fa4719e9e50>
├── 'a' --> tensor([1, 2, 3])
└── 'b' --> <Tensor 0x7fa4719e9e80>
    └── 'x' --> tensor([[4, 5],
                        [6, 7]])

new random native tensor:
tensor([[ 0.1195,  0.9845,  0.0725],
        [-1.3691,  0.3923,  0.1230]])
new random tree tensor:
<Tensor 0x7fa4719e9ee0>
├── 'a' --> tensor([[-0.7755, -0.0547, -0.8423],
│                   [-0.1307,  1.9147, -0.5424]])
└── 'b' --> <Tensor 0x7fa4720d8190>
    └── 'x' --> tensor([[-1.3317,  0.6864,  0.7633, -0.4963],
                        [ 0.7836, -0.7790,  0.4371,  1.0746],
                        [ 2.1642,  0.0471, -1.6822,  0.0486]])