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LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning

This repo contains the pytorch version code and configuration files to reproduce logicSeg. It is based on mmsegmentaion.

Usage

Installation

Please refer to get_started.md for installation and dataset preparation.

Requirement

Pytorch >= 1.8.0 & torchvision >= 0.9.0

Inference

# single-gpu testing
python tools/test.py <CONFIG_FILE> <SEG_CHECKPOINT_FILE> --eval mIoU

# multi-gpu testing
tools/dist_test.sh <CONFIG_FILE> <SEG_CHECKPOINT_FILE> <GPU_NUM> --eval mIoU

# multi-gpu, multi-scale testing
tools/dist_test.sh <CONFIG_FILE> <SEG_CHECKPOINT_FILE> <GPU_NUM> --aug-test --eval mIoU

Training

To train with pre-trained models, run:

# single-gpu training
python tools/train.py <CONFIG_FILE> --options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments]

# multi-gpu training
tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments] 

For example, to train on Mapillary Vistas 2.0 with a ResNet-101 backbone and 4 gpus, run:

tools/dist_train.sh local_configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_mapillary_v2_hiera.py 4 

Citing LogicSeg

@inproceedings{li2023logicseg,
  title={Logicseg: Parsing visual semantics with neural logic learning and reasoning},
  author={Li, Liulei and Wang, Wenguan and Yang, Yi},
  booktitle=ICCV,
  year={2023}
}

Any comments, please email: liulei.li@student.uts.edu.au.