| Name | axial-positional-embedding JSON |
| Version |
0.3.10
JSON |
| download |
| home_page | None |
| Summary | Axial Positional Embedding |
| upload_time | 2025-01-23 20:05:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2020 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
artificial intelligence
deep learning
positional embedding
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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|
## Axial Positional Embedding
[](https://badge.fury.io/py/axial-positional-embedding)
A type of positional embedding that is very effective when working with attention networks on multi-dimensional data, or for language models in general.
## Install
```bash
$ pip install axial-positional-embedding
```
## Usage
```python
import torch
from axial_positional_embedding import AxialPositionalEmbedding
pos_emb = AxialPositionalEmbedding(
dim = 512,
axial_shape = (64, 64), # axial shape will multiply up to the maximum sequence length allowed (64 * 64 = 4096)
axial_dims = (256, 256) # if not specified, dimensions will default to 'dim' for all axials and summed at the end. if specified, each axial will have the specified dimension and be concatted together. the concatted dimensions needs to sum up to the `dim` (256 + 256 = 512)
)
tokens = torch.randn(1, 1024, 512) # assume are tokens
tokens = pos_emb(tokens) + tokens # add positional embedding to token embeddings
```
A continuous version with better extrapolation ability (each axis parameterized by a 2 layer MLP)
```python
import torch
from axial_positional_embedding import ContinuousAxialPositionalEmbedding
pos_emb = ContinuousAxialPositionalEmbedding(
dim = 512,
num_axial_dims = 3
)
tokens = torch.randn(1, 8, 16, 32, 512) # say a video with 8 frames, 16 x 32 image dimension
axial_pos_emb = pos_emb((8, 16, 32)) # pass in the size from above
tokens = axial_pos_emb + tokens # add positional embedding to token embeddings
```
## Citations
```bibtex
@inproceedings{kitaev2020reformer,
title = {Reformer: The Efficient Transformer},
author = {Nikita Kitaev and Lukasz Kaiser and Anselm Levskaya},
booktitle = {International Conference on Learning Representations},
year = {2020},
url = {https://openreview.net/forum?id=rkgNKkHtvB}
}
```
```bibtex
@misc{ho2019axial,
title = {Axial Attention in Multidimensional Transformers},
author = {Jonathan Ho and Nal Kalchbrenner and Dirk Weissenborn and Tim Salimans},
year = {2019},
archivePrefix = {arXiv}
}
```
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"description": "## Axial Positional Embedding\n\n[](https://badge.fury.io/py/axial-positional-embedding)\n\nA type of positional embedding that is very effective when working with attention networks on multi-dimensional data, or for language models in general.\n\n## Install\n\n```bash\n$ pip install axial-positional-embedding\n```\n\n## Usage\n\n```python\nimport torch\nfrom axial_positional_embedding import AxialPositionalEmbedding\n\npos_emb = AxialPositionalEmbedding(\n dim = 512,\n axial_shape = (64, 64), # axial shape will multiply up to the maximum sequence length allowed (64 * 64 = 4096)\n axial_dims = (256, 256) # if not specified, dimensions will default to 'dim' for all axials and summed at the end. if specified, each axial will have the specified dimension and be concatted together. the concatted dimensions needs to sum up to the `dim` (256 + 256 = 512)\n)\n\ntokens = torch.randn(1, 1024, 512) # assume are tokens\ntokens = pos_emb(tokens) + tokens # add positional embedding to token embeddings\n```\n\nA continuous version with better extrapolation ability (each axis parameterized by a 2 layer MLP)\n\n```python\nimport torch\nfrom axial_positional_embedding import ContinuousAxialPositionalEmbedding\n\npos_emb = ContinuousAxialPositionalEmbedding(\n dim = 512,\n num_axial_dims = 3\n)\n\ntokens = torch.randn(1, 8, 16, 32, 512) # say a video with 8 frames, 16 x 32 image dimension\n\naxial_pos_emb = pos_emb((8, 16, 32)) # pass in the size from above\n\ntokens = axial_pos_emb + tokens # add positional embedding to token embeddings\n```\n\n## Citations\n\n```bibtex\n@inproceedings{kitaev2020reformer,\n title = {Reformer: The Efficient Transformer},\n author = {Nikita Kitaev and Lukasz Kaiser and Anselm Levskaya},\n booktitle = {International Conference on Learning Representations},\n year = {2020},\n url = {https://openreview.net/forum?id=rkgNKkHtvB}\n}\n```\n\n```bibtex\n@misc{ho2019axial,\n title = {Axial Attention in Multidimensional Transformers},\n author = {Jonathan Ho and Nal Kalchbrenner and Dirk Weissenborn and Tim Salimans},\n year = {2019},\n archivePrefix = {arXiv}\n}\n```\n",
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"license": "MIT License\n \n Copyright (c) 2020 Phil Wang\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
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