llmir/DANet

Dual Attention Network for Scene Segmentation (CVPR2019)

Dual Attention Network for Scene Segmentation(CVPR2019)

Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu

Introduction

We propose a Dual Attention Network (DANet) to adaptively integrate local features with their global dependencies based on the self-attention mechanism. And we achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff-10k dataset.

Cityscapes testing set result

We train our DANet-101 with only fine annotated data and submit our test results to the official evaluation server.

Usage

1. Install pytorch

• The code is tested on python3.6 and official Pytorch@commitfd25a2a, please install PyTorch from source.
• The code is modified from PyTorch-Encoding.

2. Clone the repository:

   git clone https://github.com/junfu1115/DANet.git 
   cd DANet 
   python setup.py install

3. Dataset

• Download the Cityscapes dataset and convert the dataset to 19 categories.
• Please put dataset in folder ./datasets

4 . Evaluation

• Download trained model DANet101 and put it in folder ./danet/cityscapes/model

• Evaluation code is in folder ./danet/cityscapes

• cd danet

• For single scale testing, please run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python test.py --dataset cityscapes --model danet --resume-dir cityscapes/model --base-size 2048 --crop-size 768 --workers 1 --backbone resnet101 --multi-grid --multi-dilation 4 8 16 --eval

• For multi-scale testing, please run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python test.py --dataset cityscapes --model danet --resume-dir cityscapes/model --base-size 2048 --crop-size 1024 --workers 1 --backbone resnet101 --multi-grid --multi-dilation 4 8 16 --eval --multi-scales

• If you want to visualize the result of DAN-101, you can run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python test.py --dataset cityscapes --model danet --resume-dir cityscapes/model --base-size 2048 --crop-size 768 --workers 1 --backbone resnet101 --multi-grid --multi-dilation 4 8 16

5. Evaluation Result:

   The expected scores will show as follows:

   (single scale testing denotes as 'ss' and multiple scale testing denotes as 'ms')

   DANet101 on cityscapes val set (mIoU/pAcc): 79.93/95.97 (ss) and 81.49/96.41 (ms)

6. Training:

• Training code is in folder ./danet/cityscapes
• cd danet

You can reproduce our result by run:

 CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset cityscapes --model  danet --backbone resnet101 --checkname danet101  --base-size 1024 --crop-size 768 --epochs 240 --batch-size 8 --lr 0.003 --workers 2 --multi-grid --multi-dilation 4 8 16

Note that: We adopt multiple losses in end of the network for better training.

Citation

If DANet is useful for your research, please consider citing:

@article{fu2018dual,
  title={Dual Attention Network for Scene Segmentation},
  author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
  booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}

Acknowledgement

Thanks PyTorch-Encoding, especially the Synchronized BN!