Integral search design (jointly consider backbone/head structures, design/deploy devices).

Covers mainstream handcraft saliency head design.

SOTA performance with large latency reduction on diverse hardware platforms.

0.2.0 was released in 15/11/2021:

• Support training on semantic segmentation task.
• Fix bugs in generating latency lookup table.
• Provide pre-trained model for cityscape datasets.

Please refer to changelog.md for details and release history.

Dependencies

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7
• NVIDIA GPU + CUDA

Install from a local clone

1. Clone the repo

   git clone https://github.com/guyuchao/iNAS.git

2. Install dependent packages

   conda create -n iNAS python=3.8
   conda install -c pytorch pytorch=1.7 torchvision cudatoolkit=10.2
   pip install -r requirements.txt

3. Install iNAS
   Please run the following commands in the iNAS root path to install iNAS:
   

   python setup.py develop

Folder Structure

iNAS
├── iNAS
├── experiment
├── scripts
├── options
├── datasets
│   ├── saliency
│   │   ├── DUTS-TR/            # Contains both images (.jpg) and labels (.png).
│   │   ├── DUTS-TR.lst         # Specify the image-label pair for training or testing.
│   │   ├── ECSSD/
│   │   ├── ECSSD.lst
│   │   ├── ...
│   ├── cityscape
│   │   ├── leftImg8bit/train
│   │   ├── leftImg8bit/val
│   │   ├── gtFine/
│   │   ├── train.txt
│   │   ├── val.txt
│   │   ├── ...

Common Image SOD Datasets

We provide a list of common salient object detection datasets.

Common Image Semantic Segmentation Datasets

We provide a list of common image semantic segmentation datasets.

The iNAS integrates four main steps of one-shot neural architecture search:

• Train supernet: Provide a fast performance evaluator for searching.
• Search models: Find a pareto frontier based on performance evaluator and resource evaluator.
• Convert weight/Retrain/Finetune: Promote searched model performance to its best. (We now support converting supernet weight to stand-alone models without retraining.)
• Deploy: Test stand-alone models.

Please see Tutorial.md for the basic usage of those steps in iNAS.

• Support multi-processing search (simply use data-parallel cannot increase search speed).
• Complete documentations.
• Add speed benchmark on semantic segmentation.

If you find this project useful in your research, please consider cite:

@inproceedings{gu2021inas,
  title={iNAS: Integral NAS for Device-Aware Salient Object Detection},
  author={Gu, Yu-Chao and Gao, Shang-Hua and Cao, Xu-Sheng and Du, Peng and Lu, Shao-Ping and Cheng, Ming-Ming},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4934--4944},
  year={2021}
}

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (cc-by-nc-sa), where only non-commercial usage is allowed. For commercial usage, please contact us.

The project structure is borrowed from BasicSR, and parts of implementation and evaluation codes are borrowed from Once-For-All, BASNet and BiSeNet . Thanks for these excellent projects.

If you have any questions, please email ycgu@mail.nankai.edu.cn.