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Multi-Anchor Active Domain Adaptation for Semantic Segmentation

Munan Ning*, Donghuan Lu*, Dong Wei†, Cheng Bian, Chenglang Yuan, Shuang Yu, Kai Ma, Yefeng Zheng

Introduction
Requirements
Usage
License
Notes

Introduction

This respository contains the MADA method as described in the ICCV 2021 Oral paper "Multi-Anchor Active Domain Adaptation for Semantic Segmentation".

Requirements

The code requires Pytorch >= 0.4.1 with python 3.6. The code is trained using a NVIDIA Tesla V100 with 32 GB memory. You can simply reduce the batch size in stage 2 to run on a smaller memory.

Usage

Preparation:

Download the GTA5 dataset as the source domain, and the Cityscapes dataset as the target domain.
Download the weights and features. Move features to the MADA directory.

Setup the config files.

Set the data paths
Set the pretrained model paths

Training-quick

To run the code with our weights and anchors (anchors/cluster_centroids_full_10.pkl):

python3 train_active_stage1.py
python3 train_active_stage2.py

During the training, the generated files (log file) will be written in the folder 'runs/..'.

Evaluation

Set the config file for test (configs/test_from_city_to_gta.yml):
Run:

python3 test.py

to see the results.

Training-whole process

Setting the config files.
Stage 1:
1-save_feat_source.py: get the './features/full_dataset_objective_vectors.pkl'

python3 save_feat_source.py

2-cluster_anchors_source.py: cluster the './features/full_dataset_objective_vectors.pkl' to './anchors/cluster_centroids_full_10.pkl'

python3 cluster_anchors_source.py

3-select_active_samples.py: select active samples with './anchors/cluster_centroids_full_10.pkl' to 'stage1_cac_list_0.05.txt'

python3 select_active_samples.py

4-train_active_stage1.py: train stage1 model with anchors './anchors/cluster_centroids_full_10.pkl' and active samples 'stage1_cac_list_0.05.txt', get the 'from_gta5_to_cityscapes_on_deeplab101_best_model_stage1.pkl', which is stored in the runs/active_from_gta_to_city_stage1

python3 train_active_stage1.py

Stage 2:
1-save_feat_target.py: get the './features/target_full_dataset_objective_vectors.pkl.pkl'

python3 save_feat_target.py

2-cluster_anchors_target.py: cluster the './features/target_full_dataset_objective_vectors.pkl' to './anchors/cluster_centroids_full_target_10.pkl'

python3 cluster_anchors_target.py

3-train_active_stage2.py: train stage2 model with anchors './anchors/cluster_centroids_full_target_10.pkl' and active samples 'stage1_cac_list_0.05.txt', get the 'from_gta5_to_cityscapes_on_deeplab101_best_model_stage2.pkl'

python3 train_active_stage2.py

License

MIT

The code is heavily borrowed from the CAG_UDA (https://github.com/RogerZhangzz/CAG_UDA).

If you use this code and find it usefule, please cite:

@inproceedings{ning2021multi,
  title={Multi-Anchor Active Domain Adaptation for Semantic Segmentation},
  author={Ning, Munan and Lu, Donghuan and Wei, Dong and Bian, Cheng and Yuan, Chenglang and Yu, Shuang and Ma, Kai and Zheng, Yefeng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9112--9122},
  year={2021}
}

Notes

The anchors are calcuated based on features captured by decoders.