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* Add DiNAT * Adds DiNAT + tests * Minor fixes * Added HF model * Add natten to dependencies. * Cleanup * Minor fixup * Reformat * Optional NATTEN import. * Reformat & add doc to _toctree * Reformat (finally) * Dummy objects for DiNAT * Add NAT + minor changes Adds NAT as its own independent model + docs, tests Adds NATTEN to ext deps to ensure ci picks it up. * Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests * Minor fixes. * Fix READMEs. * Requested changes to docs + minor fixes. * Requested changes. * Add NAT/DiNAT tests to layoutlm_job * Correction to Dinat doc. * Requested changes.
79 lines
4.3 KiB
Plaintext
79 lines
4.3 KiB
Plaintext
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# Dilated Neighborhood Attention Transformer
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## Overview
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DiNAT was proposed in [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001)
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by Ali Hassani and Humphrey Shi.
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It extends [NAT](nat) by adding a Dilated Neighborhood Attention pattern to capture global context,
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and shows significant performance improvements over it.
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The abstract from the paper is the following:
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*Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities,
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domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have
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also gained significant attention, thanks to their performance and easy integration into existing frameworks.
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These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA)
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or Swin Transformer's Shifted Window Self Attention. While effective at reducing self attention's quadratic complexity,
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local attention weakens two of the most desirable properties of self attention: long range inter-dependency modeling,
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and global receptive field. In this paper, we introduce Dilated Neighborhood Attention (DiNA), a natural, flexible and
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efficient extension to NA that can capture more global context and expand receptive fields exponentially at no
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additional cost. NA's local attention and DiNA's sparse global attention complement each other, and therefore we
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introduce Dilated Neighborhood Attention Transformer (DiNAT), a new hierarchical vision transformer built upon both.
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DiNAT variants enjoy significant improvements over strong baselines such as NAT, Swin, and ConvNeXt.
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Our large model is faster and ahead of its Swin counterpart by 1.5% box AP in COCO object detection,
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1.3% mask AP in COCO instance segmentation, and 1.1% mIoU in ADE20K semantic segmentation.
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Paired with new frameworks, our large variant is the new state of the art panoptic segmentation model on COCO (58.2 PQ)
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and ADE20K (48.5 PQ), and instance segmentation model on Cityscapes (44.5 AP) and ADE20K (35.4 AP) (no extra data).
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It also matches the state of the art specialized semantic segmentation models on ADE20K (58.2 mIoU),
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and ranks second on Cityscapes (84.5 mIoU) (no extra data). *
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Tips:
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- One can use the [`AutoImageProcessor`] API to prepare images for the model.
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- DiNAT can be used as a *backbone*. When `output_hidden_states = True`,
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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)`.
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Notes:
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- DiNAT depends on [NATTEN](https://github.com/SHI-Labs/NATTEN/)'s implementation of Neighborhood Attention and Dilated Neighborhood Attention.
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You can install it with pre-built wheels for Linux by referring to [shi-labs.com/natten](https://shi-labs.com/natten), or build on your system by running `pip install natten`.
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Note that the latter will likely take time to compile. NATTEN does not support Windows devices yet.
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- Patch size of 4 is only supported at the moment.
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<img
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dilated-neighborhood-attention-pattern.jpg"
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alt="drawing" width="600"/>
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<small> Neighborhood Attention with different dilation values.
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Taken from the <a href="https://arxiv.org/abs/2209.15001">original paper</a>.</small>
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This model was contributed by [Ali Hassani](https://huggingface.co/alihassanijr).
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The original code can be found [here](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer).
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## DinatConfig
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[[autodoc]] DinatConfig
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## DinatModel
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[[autodoc]] DinatModel
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- forward
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## DinatForImageClassification
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[[autodoc]] DinatForImageClassification
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- forward
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