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 0x7fee8fc96250>
├── 'a' --> tensor([1, 2, 3])
└── 'b' --> <Tensor 0x7fee8f9bba30>
    └── 'x' --> tensor([[4, 5],
                        [6, 7]])

new random native tensor:
tensor([[-0.1443, -0.4468,  0.7725],
        [ 0.5694, -2.8689,  0.5835]])
new random tree tensor:
<Tensor 0x7fee8ed54d90>
├── 'a' --> tensor([[-1.3367,  1.5045, -0.5545],
│                   [ 0.5042, -0.1285,  0.2397]])
└── 'b' --> <Tensor 0x7fee8fc84460>
    └── 'x' --> tensor([[ 0.1627,  1.3776,  0.6958, -0.3439],
                        [ 0.1151,  1.3172,  1.1026, -0.7454],
                        [-2.0887,  0.5039,  0.3364, -0.7728]])