This is the official Pytorch implementation of FocalNets:
"Focal Modulation Networks" by Jianwei Yang, Chunyuan Li, and Jianfeng Gao.
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 focal modulation have learned!
- Modulator learned by isotropic FocalNet (FocalNet-B-ISO):
- Modulator learned by multi-stage FocalNet (FocalNet-B-LRF):
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!
- [04/02/2022] Create a gradio demo in huggingface space to visualize the modulation mechanism. Check it out!
- Please follow get_started_for_image_classification to get started for image classification.
- Please follow get_started_for_object_detection to get started for object detection.
- Please follow get_started_for_semantic_segmentation to get started for semantic segmentation.
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 |
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 |
There are three steps in our FocalNets:
- Contexualization with depth-wise conv;
- Multi-scale aggregation with gating mechanism;
- 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.
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 Jianfeng Gao},
year={2022},
eprint={2203.11926},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Our codebase is built based on Swin Transformer and Focal Transformer. We thank the authors for the nicely organized code!
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