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 0x7f6d42b3d250>
├── 'a' --> tensor([1, 2, 3])
└── 'b' --> <Tensor 0x7f6d42abba30>
    └── 'x' --> tensor([[4, 5],
                        [6, 7]])

new random native tensor:
tensor([[-1.1509, -1.2110,  0.4545],
        [-0.0437,  0.0095, -0.3049]])
new random tree tensor:
<Tensor 0x7f6d42769ee0>
├── 'a' --> tensor([[-0.1972, -1.9303, -0.9943],
│                   [ 1.4699, -0.0021, -0.8833]])
└── 'b' --> <Tensor 0x7f6d42b2b460>
    └── 'x' --> tensor([[ 0.8235, -0.7934, -0.1878, -0.0060],
                        [ 0.2179, -0.0474,  1.2822, -1.1818],
                        [ 1.2408,  0.1210, -1.6937,  0.6986]])