Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data

This is the official PyTorch implementation of the domain adaptation method in our paper Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data.

Training the Multi-Target Style Transfer Network (MT-STN)

$ CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nnodes=1 --nproc_per_node=4 --node_rank=0 train_MT-STN.py

• Use tensorboard to monitor the training process:

$ tensorboard --logdir ./

Style Transfer

$ CUDA_VISIBLE_DEVICES=0 python gen_style_trans.py

Training MT-KD

• Synthetic-to-real adaptation with 1 target domain: (~28GB memory)

$ CUDA_VISIBLE_DEVICES=0 python train_MT-KD-1T-from-GTA.py

• Synthetic-to-real adaptation with 2 target domains: (~39GB memory)

$ CUDA_VISIBLE_DEVICES=0 python train_MT-KD-2T-from-GTA.py

• Synthetic-to-real adaptation with 3 target domains: (~50GB memory A100 80G)

$ CUDA_VISIBLE_DEVICES=0 python train_MT-KD-3T-from-GTA.py

• Real-to-real adaptation with 1 target domain: (~22GB memory)

$ CUDA_VISIBLE_DEVICES=0 python train_MT-KD-1T-from-Cityscapes.py

• Real-to-real adaptation with 2 target domains: (~33GB memory)

$ CUDA_VISIBLE_DEVICES=0 python train_MT-KD-2T-from-Cityscapes.py

Training UT-KD

$ CUDA_VISIBLE_DEVICES=0 python train_UT-KD.py

Evaluation

$ CUDA_VISIBLE_DEVICES=0 python evaluation.py

Pretrained models

License

This repo is distributed under MIT License. The code can be used for academic purposes only.