/FocalNet

[NeurIPS 2022] Official code for "Focal Modulation Networks"

Primary LanguagePythonMIT LicenseMIT

This is the official Pytorch implementation of FocalNets:

"Focal Modulation Networks" by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan and Jianfeng Gao.

PWC PWC PWC PWC

News

  • [11/07/2023] Researchers showed that Focal-UNet beats previous methods on several earth system analysis benchmarks. Check out their code, paper, and project!
  • [06/30/2023] 💥 Please find FocalNet-DINO checkpoints from huggingface. The old links are deprecated.
  • [04/26/2023] By combining with FocalNet-Huge backbone, Focal-Stable-DINO achieves 64.8 AP on COCO test-dev without any test time augmentation! Check our Technical Report for more details!
  • [02/13/2023] FocalNet has been integrated to Keras, check out the tutorial!
  • [01/18/2023] Checkout a curated paper list which introduce networks beyond attention based on modern convolution and modulation!
  • [01/01/2023] Researchers showed that Focal-UNet beats Swin-UNet on several medical image segmentation benchmarks. Check out their code and paper, and happy new year!
  • [12/16/2022] 💥 We are pleased to release our FocalNet-Large-DINO checkpoint pretrained on Object365 and finetuned on COCO, which help to get 63.5 mAP without tta on COCO minival! Check it out!
  • [11/14/2022] We created a new repo FocalNet-DINO to hold the code to reproduce the object detection performance with DINO. We will be releasing the object detection code and checkpoints there. Stay tunned!
  • [11/13/2022] 💥 We release our large, xlarge and huge models pretrained on ImageNet-22K, including the one we used to achieve the SoTA on COCO object detection leaderboard!
  • [11/02/2022] We wrote a blog post to introduce the insights and techniques behind our FocalNets in a plain way, check it out!
  • [10/31/2022] 💥 We achieved new SoTA with 64.2 box mAP on COCO minival and 64.3 64.4 box mAP on COCO test-dev based on the powerful OD method DINO! We used huge model size (700M) beating much larger attention-based models like SwinV2-G and BEIT-3. Checkoout our new version and stay tuned!
  • [09/20/2022] Our FocalNet has been accepted by NeurIPS 2022!
  • [04/02/2022] Create a gradio demo in huggingface space to visualize the modulation mechanism. Check it out!

Introduction

We propose FocalNets: Focal Modulation Networks, an attention-free architecture that achieves superior performance than SoTA self-attention (SA) methods across various vision benchmarks. SA is an first interaction, last aggregation (FILA) process as shown above. Our Focal Modulation inverts the process by first aggregating, last interaction (FALI). This inversion brings several merits:

  • Translation-Invariance: It is performed for each target token with the context centered around it.
  • Explicit input-dependency: The modulator is computed by aggregating the short- and long-rage context from the input and then applied to the target token.
  • Spatial- and channel-specific: It first aggregates the context spatial-wise and then channel-wise, followed by an element-wise modulation.
  • Decoupled feature granularity: Query token preserves the invidual information at finest level, while coarser context is extracted surrounding it. They two are decoupled but connected through the modulation operation.
  • Easy to implement: We can implement both context aggregation and interaction in a very simple and light-weight way. It does not need softmax, multiple attention heads, feature map rolling or unfolding, etc.

Before getting started, see what our FocalNets have learned to perceive images and where to modulate!

Finally, FocalNets are built with convolutional and linear layers, but goes beyond by proposing a new modulation mechanism that is simple, generic, effective and efficient. We hereby recommend:

Focal-Modulation May be What We Need for Visual Modeling!

Getting Started

Benchmarking

Image Classification on ImageNet-1K

  • Strict comparison with multi-scale Swin and Focal Transformers:
Model Depth Dim Kernels #Params. (M) FLOPs (G) Throughput (imgs/s) Top-1 Download
FocalNet-T [2,2,6,2] 96 [3,5] 28.4 4.4 743 82.1 ckpt/config/log
FocalNet-T [2,2,6,2] 96 [3,5,7] 28.6 4.5 696 82.3 ckpt/config/log
FocalNet-S [2,2,18,2] 96 [3,5] 49.9 8.6 434 83.4 ckpt/config/log
FocalNet-S [2,2,18,2] 96 [3,5,7] 50.3 8.7 406 83.5 ckpt/config/log
FocalNet-B [2,2,18,2] 128 [3,5] 88.1 15.3 280 83.7 ckpt/config/log
FocalNet-B [2,2,18,2] 128 [3,5,7] 88.7 15.4 269 83.9 ckpt/config/log
  • Strict comparison with isotropic ViT models:
Model Depth Dim Kernels #Params. (M) FLOPs (G) Throughput (imgs/s) Top-1 Download
FocalNet-T 12 192 [3,5,7] 5.9 1.1 2334 74.1 ckpt/config/log
FocalNet-S 12 384 [3,5,7] 22.4 4.3 920 80.9 ckpt/config/log
FocalNet-B 12 768 [3,5,7] 87.2 16.9 300 82.4 ckpt/config/log

ImageNet-22K Pretraining

Model Depth Dim Kernels #Params. (M) Download
FocalNet-L [2,2,18,2] 192 [5,7,9] 207 ckpt/config
FocalNet-L [2,2,18,2] 192 [3,5,7,9] 207 ckpt/config
FocalNet-XL [2,2,18,2] 256 [5,7,9] 366 ckpt/config
FocalNet-XL [2,2,18,2] 256 [3,5,7,9] 366 ckpt/config
FocalNet-H [2,2,18,2] 352 [3,5,7] 687 ckpt/config
FocalNet-H [2,2,18,2] 352 [3,5,7,9] 689 ckpt/config

NOTE: We reorder the class names in imagenet-22k so that we can directly use the first 1k logits for evaluating on imagenet-1k. We remind that the 851th class (label=850) in imagenet-1k is missed in imagenet-22k. Please refer to this labelmap. More discussion found in this issue.

Object Detection on COCO

Backbone Kernels Lr Schd #Params. (M) FLOPs (G) box mAP mask mAP Download
FocalNet-T [9,11] 1x 48.6 267 45.9 41.3 ckpt/config/log
FocalNet-T [9,11] 3x 48.6 267 47.6 42.6 ckpt/config/log
FocalNet-T [9,11,13] 1x 48.8 268 46.1 41.5 ckpt/config/log
FocalNet-T [9,11,13] 3x 48.8 268 48.0 42.9 ckpt/config/log
FocalNet-S [9,11] 1x 70.8 356 48.0 42.7 ckpt/config/log
FocalNet-S [9,11] 3x 70.8 356 48.9 43.6 ckpt/config/log
FocalNet-S [9,11,13] 1x 72.3 365 48.3 43.1 ckpt/config/log
FocalNet-S [9,11,13] 3x 72.3 365 49.3 43.8 ckpt/config/log
FocalNet-B [9,11] 1x 109.4 496 48.8 43.3 ckpt/config/log
FocalNet-B [9,11] 3x 109.4 496 49.6 44.1 ckpt/config/log
FocalNet-B [9,11,13] 1x 111.4 507 49.0 43.5 ckpt/config/log
FocalNet-B [9,11,13] 3x 111.4 507 49.8 44.1 ckpt/config/log
  • Other detection methods
Backbone Kernels Method Lr Schd #Params. (M) FLOPs (G) box mAP Download
FocalNet-T [11,9,9,7] Cascade Mask R-CNN 3x 87.1 751 51.5 ckpt/config/log
FocalNet-T [11,9,9,7] ATSS 3x 37.2 220 49.6 ckpt/config/log
FocalNet-T [11,9,9,7] Sparse R-CNN 3x 111.2 178 49.9 ckpt/config/log

Semantic Segmentation on ADE20K

  • Resolution 512x512 and Iters 160k
Backbone Kernels Method #Params. (M) FLOPs (G) mIoU mIoU (MS) Download
FocalNet-T [9,11] UPerNet 61 944 46.5 47.2 ckpt/config/log
FocalNet-T [9,11,13] UPerNet 61 949 46.8 47.8 ckpt/config/log
FocalNet-S [9,11] UPerNet 83 1035 49.3 50.1 ckpt/config/log
FocalNet-S [9,11,13] UPerNet 84 1044 49.1 50.1 ckpt/config/log
FocalNet-B [9,11] UPerNet 124 1180 50.2 51.1 ckpt/config/log
FocalNet-B [9,11,13] UPerNet 126 1192 50.5 51.4 ckpt/config/log

Visualizations

There are three steps in our FocalNets:

  1. Contexualization with depth-wise conv;
  2. Multi-scale aggregation with gating mechanism;
  3. Modulator derived from context aggregation and projection.

We visualize them one by one.

  • Depth-wise convolution kernels learned in FocalNets:

Yellow colors represent higher values. Apparently, FocalNets learn to gather more local context at earlier stages while more global context at later stages.

  • Gating maps at last layer of FocalNets for different input images:

From left to right, the images are input image, gating map for focal level 1,2,3 and the global context. Clearly, our model has learned where to gather the context depending on the visual contents at different locations.

  • Modulator learned in FocalNets for different input images:

The modulator derived from our model automatically learns to focus on the foreground regions.

For visualization by your own, please refer to visualization notebook.

Citation

If you find this repo useful to your project, please consider to cite it with following bib:

@misc{yang2022focal,
      title={Focal Modulation Networks}, 
      author={Jianwei Yang and Chunyuan Li and Xiyang Dai and Jianfeng Gao},
      journal={Advances in Neural Information Processing Systems (NeurIPS)},
      year={2022}
}

Acknowledgement

Our codebase is built based on Swin Transformer and Focal Transformer. To achieve the SoTA object detection performance, we heavily rely on the most advanced method DINO and the advices from the authors. We thank the authors for the nicely organized code!

Contributing

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