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Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation

Official code for the ICCV 2023 paper Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation. The code is organized using PyTorch Lightning.

Abstract

Standard unsupervised domain adaptation methods adapt models from a source to a target domain using labeled source data and unlabeled target data jointly. In model adaptation, on the other hand, access to the labeled source data is prohibited, i.e., only the source-trained model and unlabeled target data are available. We investigate normal-to-adverse condition model adaptation for semantic segmentation, whereby image-level correspondences are available in the target domain. The target set consists of unlabeled pairs of adverse- and normal-condition street images taken at GPS-matched locations. Our method--CMA--leverages such image pairs to learn condition-invariant features via contrastive learning. In particular, CMA encourages features in the embedding space to be grouped according to their condition-invariant semantic content and not according to the condition under which respective inputs are captured. To obtain accurate cross-domain semantic correspondences, we warp the normal image to the viewpoint of the adverse image and leverage warp-confidence scores to create robust, aggregated features. With this approach, we achieve state-of-the-art semantic segmentation performance for model adaptation on several normal-to-adverse adaptation benchmarks, such as ACDC and Dark Zurich. We also evaluate CMA on a newly procured adverse-condition generalization benchmark and report favorable results compared to standard unsupervised domain adaptation methods, despite the comparative handicap of CMA due to source data inaccessibility.

Usage

Requirements

The code is run with Python 3.10.4. To install the packages, use:

pip install -r requirements.txt

Optional

Local correlation is implemented through this custom CUDA extension. By default, the extension is built just in time using Ninja. In case of problems, the extension can be alternatively pre-installed in the environment (see also the README of the linked repo):

pip install spatial-correlation-sampler

Set Data Directory

The following environment variable must be set:

export DATA_DIR=/path/to/data/dir

Download the Data

Before running the code, download and extract the respective datasets to the directory $DATA_DIR.

ACDC

Download rgb_anon_trainvaltest.zip and gt_trainval.zip from here and extract them to $DATA_DIR/ACDC.

$DATA_DIR
├── ACDC
│   ├── rgb_anon
│   │   ├── fog
│   │   ├── night
│   │   ├── rain
│   │   ├── snow
│   ├── gt
│   │   ├── fog
│   │   ├── night
│   │   ├── rain
│   │   ├── snow
├── ...

Dark Zurich

Download Dark_Zurich_train_anon.zip, Dark_Zurich_val_anon.zip, and Dark_Zurich_test_anon_withoutGt.zip from here and extract them to $DATA_DIR/DarkZurich.

$DATA_DIR
├── DarkZurich
│   ├── rgb_anon
│   │   ├── train
│   │   ├── val
│   │   ├── val_ref
│   │   ├── test
│   │   ├── test_ref
│   ├── gt
│   │   ├── val
├── ...

RobotCar

Download all data from here and save them to $DATA_DIR/RobotCar. As mentioned in the corresponding README.txt, the images must be downloaded from this link.

$DATA_DIR
├── RobotCar
│   ├── images
│   │   ├── dawn
│   │   ├── dusk
│   │   ├── night
│   │   ├── night-rain
│   │   ├── ...
│   ├── correspondence_data
│   │   ├── ...
│   ├── segmented_images
│   │   ├── training
│   │   ├── validation
│   │   ├── testing
├── ...

Download the Pretrained Weights

The Cityscapes-pretrained SegFormer weights (segformer.b5.1024x1024.city.160k.pth) are required for CMA. Download them from the SegFormer repository and save them to ./pretrained_models/.

Model Checkpoints and Results

We provide the following model checkpoints and validation set predictions:

Method	Architecture	Dataset	Test mIoU	Config	Checkpoint	Predictions
CMA	SegFormer	ACDC	69.1	config	model	ACDC val
CMA	DeepLabv2	ACDC	50.4	config	model	ACDC val
CMA	SegFormer	Dark Zurich	53.6	config	model	Dark Zurich val
CMA	SegFormer	RobotCar	54.3	config	model	RobotCar val

Create Pseudo-Labels (Optional)

Before training CMA, optionally create pseudo-labels using the source model. For example for a SegFormer architecture on ACDC:

python -m tools.run generate_pl --config configs/cma_segformer_acdc.yaml --trainer.accelerator gpu

This will save the pseudo-labels to $DATA_DIR/pseudo_labels. If this step is skipped, precomputed pseudo-labels will be automatically downloaded to $DATA_DIR/pseudo_labels on the first training run.

Training

To train CMA (with AMP), e.g. for a SegFormer architecture on ACDC, use the following command:

python -m tools.run fit --config configs/cma_segformer_acdc.yaml --trainer.accelerator gpu --trainer.precision 16

Note that a GPU with around 20 GB memory is needed to train CMA. See configs/ for config files for other datasets and architectures. See the Lightning CLI Docs for more information on how to control hyperparameters etc.

Testing

To evaluate CMA, provide the model checkpoint as argument, e.g. for a SegFormer architecture on RobotCar:

python -m tools.run test --config configs/cma_segformer_robotcar.yaml --trainer.accelerator gpu --ckpt_path /path/to/checkpoint.ckpt

For ACDC and Dark Zurich, this command would compute the performance on the validation set. To get test set scores, predictions are evaluated on the respective evaluation servers: ACDC and Dark Zurich. To create and save test predictions for e.g. ACDC, use this command:

python -m tools.run predict --config configs/cma_segformer_acdc.yaml --trainer.accelerator gpu --ckpt_path /path/to/checkpoint.ckpt

ACG Benchmark

To evaluate a model on the ACG benchmark, first download the filename lists and instructions here: ACG Benchmark

See the README file of the downloaded ACG bundle for details on how to retrieve and arrange the necessary datasets. In summary, the file structure should look as follows:

$DATA_DIR
├── ACG
│   ├── ...
├── WildDash2
│   ├── ...
├── bdd100k
│   ├── ...
├── Foggy_Driving
│   ├── ...
├── Foggy_Zurich
│   ├── ...

Before running the evaluation, uncomment the respective lines in the config file (in the dataloader and the metrics settings). Then test the model as usual:

python -m tools.run test --config configs/cma_segformer_acdc.yaml --trainer.accelerator gpu --ckpt_path /path/to/checkpoint.ckpt

Citation

If you find this code useful in your research, please consider citing the paper:

@inproceedings{bruggemann2023contrastive,
  title={Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation},
  author={Bruggemann, David and Sakaridis, Christos and Broedermann, Tim and Van Gool, Luc},
  booktitle={ICCV},
  year={2023}
}

License

This repository is released under the MIT license. However, care should be taken to adopt appropriate licensing for third-party code in this repository. Third-party code is marked accordingly.