| Name | gotennet-pytorch JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | GotenNet in Pytorch |
| upload_time | 2024-12-20 14:27:50 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 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
attention mechanism
deep learning
molecules
se3 equivariance
transformers
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<img src="./gotennet.png" width="450px"></img>
## GotenNet - Pytorch
Implementation of <a href="https://openreview.net/forum?id=5wxCQDtbMo">GotenNet</a>, new SOTA 3d equivariant transformer, in Pytorch
I know a lot of researchers have moved on from geometric learning after Alphafold3. However, I just cannot help but <a href="https://arxiv.org/abs/2410.11443">wonder</a>. Hedging my bets
## Install
```bash
$ pip install gotennet-pytorch
```
## Usage
```python
import torch
from gotennet_pytorch import GotenNet
model = GotenNet(
dim = 256,
max_degree = 2,
depth = 1,
heads = 2,
dim_head = 32,
dim_edge_refinement = 256,
return_coors = False
)
atom_ids = torch.randint(0, 14, (1, 12))
coors = torch.randn(1, 12, 3)
adj_mat = torch.randint(0, 2, (1, 12, 12)).bool()
lens = torch.randint(1, 12, (1,))
invariant, coors_out = model(atom_ids, adj_mat = adj_mat, coors = coors, lens = lens)
```
## Citations
```bibtex
@inproceedings{anonymous2024rethinking,
title = {Rethinking Efficient 3D Equivariant Graph Neural Networks},
author = {Anonymous},
booktitle = {Submitted to The Thirteenth International Conference on Learning Representations},
year = {2024},
url = {https://openreview.net/forum?id=5wxCQDtbMo},
note = {under review}
}
```
```bibtex
@inproceedings{Zhou2024ValueRL,
title = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
author = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
year = {2024},
url = {https://api.semanticscholar.org/CorpusID:273532030}
}
```
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"description": "<img src=\"./gotennet.png\" width=\"450px\"></img>\n\n## GotenNet - Pytorch\n\nImplementation of <a href=\"https://openreview.net/forum?id=5wxCQDtbMo\">GotenNet</a>, new SOTA 3d equivariant transformer, in Pytorch\n\nI know a lot of researchers have moved on from geometric learning after Alphafold3. However, I just cannot help but <a href=\"https://arxiv.org/abs/2410.11443\">wonder</a>. Hedging my bets\n\n## Install\n\n```bash\n$ pip install gotennet-pytorch\n```\n\n## Usage\n\n```python\nimport torch\nfrom gotennet_pytorch import GotenNet\n\nmodel = GotenNet(\n dim = 256,\n max_degree = 2,\n depth = 1,\n heads = 2,\n dim_head = 32,\n dim_edge_refinement = 256,\n return_coors = False\n)\n\natom_ids = torch.randint(0, 14, (1, 12))\ncoors = torch.randn(1, 12, 3)\nadj_mat = torch.randint(0, 2, (1, 12, 12)).bool()\nlens = torch.randint(1, 12, (1,))\n\ninvariant, coors_out = model(atom_ids, adj_mat = adj_mat, coors = coors, lens = lens)\n```\n\n## Citations\n\n```bibtex\n@inproceedings{anonymous2024rethinking,\n title = {Rethinking Efficient 3D Equivariant Graph Neural Networks},\n author = {Anonymous},\n booktitle = {Submitted to The Thirteenth International Conference on Learning Representations},\n year = {2024},\n url = {https://openreview.net/forum?id=5wxCQDtbMo},\n note = {under review}\n}\n```\n\n```bibtex\n@inproceedings{Zhou2024ValueRL,\n title = {Value Residual Learning For Alleviating Attention Concentration In Transformers},\n author = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},\n year = {2024},\n url = {https://api.semanticscholar.org/CorpusID:273532030}\n}\n```\n",
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