06f1fd69a6
5.2 KiB
This model was published in HF papers on 2022-09-29 and contributed to Hugging Face Transformers on 2022-11-18.
Dilated Neighborhood Attention Transformer
Overview
DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi.
It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.
The abstract from the paper is the following:
Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have also gained significant attention, thanks to their performance and easy integration into existing frameworks. These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin Transformer's Shifted Window Self Attention. While effective at reducing self attention's quadratic complexity, local attention weakens two of the most desirable properties of self attention: long range inter-dependency modeling, and global receptive field. In this paper, we introduce Dilated Neighborhood Attention (DiNA), a natural, flexible and efficient extension to NA that can capture more global context and expand receptive fields exponentially at no additional cost. NA's local attention and DiNA's sparse global attention complement each other, and therefore we introduce Dilated Neighborhood Attention Transformer (DiNAT), a new hierarchical vision transformer built upon both. DiNAT variants enjoy significant improvements over strong baselines such as NAT, Swin, and ConvNeXt. Our large model is faster and ahead of its Swin counterpart by 1.5% box AP in COCO object detection, 1.3% mask AP in COCO instance segmentation, and 1.1% mIoU in ADE20K semantic segmentation. Paired with new frameworks, our large variant is the new state of the art panoptic segmentation model on COCO (58.2 PQ) and ADE20K (48.5 PQ), and instance segmentation model on Cityscapes (44.5 AP) and ADE20K (35.4 AP) (no extra data). It also matches the state of the art specialized semantic segmentation models on ADE20K (58.2 mIoU), and ranks second on Cityscapes (84.5 mIoU) (no extra data).
Neighborhood Attention with different dilation values. Taken from the original paper.
This model was contributed by Ali Hassani. The original code can be found here.
Usage tips
DiNAT 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:
- DiNAT depends on NATTEN's implementation of Neighborhood Attention and Dilated 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 DiNAT.
- [
DinatForImageClassification] 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.
DinatConfig
autodoc DinatConfig
DinatModel
autodoc DinatModel - forward
DinatForImageClassification
autodoc DinatForImageClassification - forward