Practical overview
When it fits
Use it when your PyTorch vision model needs a Halo attention layer and you want to configure its neighborhood and head dimensions directly.
Who it is for
- PyTorch computer-vision developers adding local attention to model feature layers.
- Researchers and practitioners prototyping visual backbones with configurable attention blocks.
Documented uses
- Add a local self-attention layer to a computer-vision model's feature maps.
- Prototype a visual backbone using Halo attention.
- Experiment with block size, halo size, feature dimensions and attention heads.
Requirements & limits
The repository is public, identifies an MIT license, and provides the package installation command `pip install halonet-pytorch`.
- This is an attention-layer implementation; the repository says it contains little beyond that layer.
- The example requires feature-map dimensions divisible by the selected block size.
- The documented example uses CUDA for both the layer and input tensor.
- The example preserves the shown feature-map shape from input to output.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install and instantiate HaloAttention
Set up the library and construct the documented attention layer for a feature map.
Before you start
- Python environment with PyTorch available.
- Feature-map dimensions divisible by the chosen block size.
- The example places the module and input tensor on CUDA.
Steps
Install the package with the command below.
pip install halonet-pytorchImport
torchandHaloAttentionfromhalonet_pytorch.Construct
HaloAttentionwith feature dimension, block size, halo size, head dimension and number of heads; the example uses 512, 8, 4, 64 and 4 respectively.Create the example input as a tensor shaped
(1, 512, 32, 32)and apply the layer.
Expected outcome
The documented example produces an output feature map shaped `(1, 512, 32, 32)`.
Guide limitations
- The README presents a layer example, not a complete model or training workflow.
- The example uses CUDA; no alternative device setup is described.
- The feature map must be divisible by the block size.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 8 October 2026
- Original website title
- lucidrains/halonet-pytorch · Official homepage title · retained in its source language