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Multi-Anchor Active Domain Adaptation for Semantic Segmentation (ICCV 2021 Oral)

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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

[[Paper](https://arxiv.org/abs/2108.08012)]
[[PPT](https://docs.google.com/presentation/d/1sJQ0OtPH6PhDC5W4BrVGvViit7crao2y/edit?usp=sharing&ouid=108503405808129646625&rtpof=true&sd=true)]
[[Graphic Abstract](https://docs.google.com/presentation/d/1tgatWywexH-KIqdI5nePC9iP1NqwfcYa/edit?usp=sharing&ouid=108503405808129646625&rtpof=true&sd=true)]

## Table of Contents

- [Introduction](#introduction)
- [Requirements](#requirements)
- [Usage](#usage)
- [License](#license)
- [Notes](#notes)

## Introduction

This respository contains the MADA method as described in the ICCV 2021 Oral paper ["Multi-Anchor Active Domain Adaptation for Semantic Segmentation"](https://arxiv.org/abs/2108.08012).

## 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

1. Preparation:
* Download the [GTA5](https://download.visinf.tu-darmstadt.de/data/from_games/) dataset as the source domain, and the [Cityscapes](https://www.cityscapes-dataset.com/) dataset as the target domain.
* Download the [weights](https://drive.google.com/drive/folders/16DxIRuo06afRp2FpXvWg9nAeYiTL25yP?usp=sharing) and [features](https://drive.google.com/drive/folders/1ybhJUTh7y1QcOnpNz0jnxkpz5TTtO55s?usp=sharing). Move features to the MADA directory.

2. Setup the config files.
* Set the data paths
* Set the pretrained model paths

3. 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/..'.

4. Evaluation
* Set the config file for test (configs/test_from_city_to_gta.yml):
* Run:
~~~~
python3 test.py
~~~~
to see the results.

5. 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](LICENSE)

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.

In this paper, we utilize the more powerful decoder in DeeplabV3+, it may cause somewhere unfair. So we strongly recommend the [ProDA](https://github.com/microsoft/ProDA) which utilize origin DeeplabV2 decoder.

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