
Neighborhood Attention Transformer
Overview
NAT was proposed in Neighborhood Attention Transformer
by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern.
The abstract from the paper is the following:
*We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.
NA is a pixel-wise operation, localizing self attention (SA) to the nearest neighboring pixels, and therefore enjoys a
linear time and space complexity compared to the quadratic complexity of SA. The sliding-window pattern allows NA's
receptive field to grow without needing extra pixel shifts, and preserves translational equivariance, unlike
Swin Transformer's Window Self Attention (WSA). We develop NATTEN (Neighborhood Attention Extension), a Python package
with efficient C++ and CUDA kernels, which allows NA to run up to 40% faster than Swin's WSA while using up to 25% less
memory. We further present Neighborhood Attention Transformer (NAT), a new hierarchical transformer design based on NA
that boosts image classification and downstream vision performance. Experimental results on NAT are competitive;
NAT-Tiny reaches 83.2% top-1 accuracy on ImageNet, 51.4% mAP on MS-COCO and 48.4% mIoU on ADE20K, which is 1.9%
ImageNet accuracy, 1.0% COCO mAP, and 2.6% ADE20K mIoU improvement over a Swin model with similar size. *

 Neighborhood Attention compared to other attention patterns.
Taken from the original paper.
This model was contributed by Ali Hassani.
The original code can be found here.
Usage tips

One can use the [AutoImageProcessor] API to prepare images for the model.
NAT can be used as a backbone. When output_hidden_states = True,
it will output both hidden_states and reshaped_hidden_states.
The reshaped_hidden_states have a shape of (batch, num_channels, height, width) rather than
(batch_size, height, width, num_channels).

Notes:
- NAT depends on NATTEN's implementation of Neighborhood Attention.
You can install it with pre-built wheels for Linux by referring to shi-labs.com/natten,
or build on your system by running pip install natten.
Note that the latter will likely take time to compile. NATTEN does not support Windows devices yet.
- Patch size of 4 is only supported at the moment.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with NAT.

[NatForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
NatConfig
[[autodoc]] NatConfig
NatModel
[[autodoc]] NatModel
    - forward
NatForImageClassification
[[autodoc]] NatForImageClassification
    - forward