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Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

Paper

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
valeo.ai, France
IEEE International Conference on Computer Vision (ICCV), 2021 (Poster)

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

@inproceedings{saporta2021mtaf,
  title={Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation},
  author={Saporta, Antoine and Vu, Tuan-Hung and Cord, Mathieu and P{\'e}rez, Patrick},
  booktitle={ICCV},
  year={2021}
}

Abstract

In this work, we address the task of unsupervised domain adaptation (UDA) for semantic segmentation in presence of multiple target domains: The objective is to train a single model that can handle all these domains at test time. Such a multi-target adaptation is crucial for a variety of scenarios that real-world autonomous systems must handle. It is a challenging setup since one faces not only the domain gap between the labeled source set and the unlabeled target set, but also the distribution shifts existing within the latter among the different target domains. To this end, we introduce two adversarial frameworks: (i) multi-discriminator, which explicitly aligns each target domain to its counterparts, and (ii) multi-target knowledge transfer, which learns a target-agnostic model thanks to a multi-teacher/single-student distillation mechanism.The evaluation is done on four newly-proposed multi-target benchmarks for UDA in semantic segmentation. In all tested scenarios, our approaches consistently outperform baselines, setting competitive standards for the novel task.

Preparation

Pre-requisites

Installation

  1. Clone the repo:
$ git clone https://github.com/valeoai/MTAF
$ cd MTAF
  1. Install OpenCV if you don't already have it:
$ conda install -c menpo opencv

  1. Install NVIDIA Apex if you don't already have it: follow the instructions on: https://github.com/NVIDIA/apex

  2. Install this repository and the dependencies using pip:

$ pip install -e <root_dir>

With this, you can edit the MTAF code on the fly and import function and classes of MTAF in other project as well.

  1. Optional. To uninstall this package, run:
$ pip uninstall MTAF

Datasets

By default, the datasets are put in <root_dir>/data. We use symlinks to hook the MTAF codebase to the datasets. An alternative option is to explicitlly specify the parameters DATA_DIRECTORY_SOURCE and DATA_DIRECTORY_TARGET in YML configuration files.

<root_dir>/data/GTA5/                               % GTA dataset root
<root_dir>/data/GTA5/images/                        % GTA images
<root_dir>/data/GTA5/labels/                        % Semantic segmentation labels
...

<root_dir>/data/cityscapes/                         % Cityscapes dataset root
<root_dir>/data/cityscapes/leftImg8bit              % Cityscapes images
<root_dir>/data/cityscapes/leftImg8bit/train
<root_dir>/data/cityscapes/leftImg8bit/val
<root_dir>/data/cityscapes/gtFine                   % Semantic segmentation labels
<root_dir>/data/cityscapes/gtFine/train
<root_dir>/data/cityscapes/gtFine/val
...

<root_dir>/data/mapillary/                          % Mapillary dataset root
<root_dir>/data/mapillary/train                     % Mapillary train set
<root_dir>/data/mapillary/train/images
<root_dir>/data/mapillary/validation                % Mapillary validation set
<root_dir>/data/mapillary/validation/images
<root_dir>/data/mapillary/validation/labels
...

<root_dir>/data/IDD/                         % IDD dataset root
<root_dir>/data/IDD/leftImg8bit              % IDD images
<root_dir>/data/IDD/leftImg8bit/train
<root_dir>/data/IDD/leftImg8bit/val
<root_dir>/data/IDD/gtFine                   % Semantic segmentation labels
<root_dir>/data/IDD/gtFine/val
...

Pre-trained models

Pre-trained models can be downloaded here and put in <root_dir>/pretrained_models

Running the code

For evaluation, execute:

$ cd <root_dir>/mtaf/scripts
$ python test.py --cfg ./configs/gta2cityscapes_mapillary_baseline_pretrained.yml
$ python test.py --cfg ./configs/gta2cityscapes_mapillary_mdis_pretrained.yml
$ python test.py --cfg ./configs/gta2cityscapes_mapillary_mtkt_pretrained.yml

Training

For the experiments done in the paper, we used pytorch 1.3.1 and CUDA 10.0. To ensure reproduction, the random seed has been fixed in the code. Still, you may need to train a few times to reach the comparable performance.

By default, logs and snapshots are stored in <root_dir>/experiments with this structure:

<root_dir>/experiments/logs
<root_dir>/experiments/snapshots

To train the multi-target baseline:

$ cd <root_dir>/mtaf/scripts
$ python train.py --cfg ./configs/gta2cityscapes_mapillary_baseline.yml

To train the Multi-Discriminator framework:

$ cd <root_dir>/mtaf/scripts
$ python train.py --cfg ./configs/gta2cityscapes_mapillary_mdis.yml

To train the Multi-Target Knowledge Transfer framework:

$ cd <root_dir>/mtaf/scripts
$ python train.py --cfg ./configs/gta2cityscapes_mapillary_mtkt.yml

Testing

To test the multi-target baseline:

$ cd <root_dir>/mtaf/scripts
$ python test.py --cfg ./configs/gta2cityscapes_mapillary_baseline.yml

To test the Multi-Discriminator framework:

$ cd <root_dir>/mtaf/scripts
$ python test.py --cfg ./configs/gta2cityscapes_mapillary_mdis.yml

To test the Multi-Target Knowledge Transfer framework:

$ cd <root_dir>/mtaf/scripts
$ python test.py --cfg ./configs/gta2cityscapes_mapillary_mtkt.yml

Acknowledgements

This codebase is heavily borrowed from ADVENT.

License

MTAF is released under the Apache 2.0 license.