LinZhuoChen/SGNet - Githubissues

Spatial information guided Convolution for Real-Time RGBD Semantic Segmentation

Lin-Zhuo Chen, Zheng Lin, Ziqin Wang, Yong-Liang Yang and Ming-Ming Cheng
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The official repo of the TIP 2021 paper `` Spatial information guided Convolution for Real-Time RGBD Semantic Segmentation.

Results on NYUDv2 Dataset

Speed is related to the hardware spec (e.g. CPU, GPU, RAM, etc), so it is hard to make an equal comparison.

I get the following results under NVIDIA 1080TI GPU, Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz:

Model	mIoU(480x640)	mIoU(MS)	FPS(480x640)	FPS(425x560)
SGNet(Res50)	47.7%	48.6%	35	39
SGNet	49.8%	51.1%	26	28
SGNet_ASPP	50.2%	51.1%	24	26

If you want to measure speed on more advanced graphics card (such as 2080ti), you can use the environment of pytorch 0.4.1 CUDA 9.2 to measure inference speed.

Prerequisites

Environments

PyTorch == 0.4.1
tqdm
CUDA==8.0
CUDNN=7.1.4
pillow
numpy
tensorboardX
tqdm
Trained model and dataset

Download NYUDv2 dataset and trained model:

	Dataset	model	model	model
BaiduDrive(passwd: scon)	NYUDv2	SGNet_Res50	SGNet	SGNet_ASPP

Usage

Put the pretrained model into pretrained_weights folder and unzip the dataset into dataset folder.

To compile the InPlace-ABN and S-Conv operation, please run:

## compile InPlace-ABN 
cd graphs/ops/libs
sh build.sh
python build.py
## compile S-Conv
cd ..
sh make.sh

Modify the config in configs/sgnet_nyud_test.json (mainly check "trained_model_path"). To test the model with imput size $480 \times 640$, please run:

## SGNet
python main.py ./configs/sgnet_nyud_test.json

## SGNet_ASPP
python main.py ./configs/sgnet_aspp_nyud_test.json

## SGNet_Res50
python main.py ./configs/sgnet_res50_nyud_test.json

You can run the follow command to test the model inference speed, input the image size such as 480 x 640:

## SGNet
python main.py ./configs/sgnet_nyud_fps.json

## SGNet_ASPP
python main.py ./configs/sgnet_aspp_nyud_fps.json

## SGNet_Res50
python main.py ./configs/sgnet_res50_nyud_fps.json

Citation

If you find this work is useful for your research, please cite our paper:

@article{21TIP-SGNet,   
  author={Lin-Zhuo Chen and Zheng Lin and Ziqin Wang and Yong-Liang Yang and Ming-Ming Cheng},   
  journal={IEEE Transactions on Image Processing},    
  title={Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation}, 
  year={2021},   
  volume={30},
  pages={2313-2324},  
  doi={10.1109/TIP.2021.3049332} 
}