problem of ‘def get_rare_class_sample’

DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

by Lukas Hoyer, Dengxin Dai, and Luc Van Gool

[CVPR22 Paper] [Extension Paper]

🔔 News:

[2023-09-26] We are happy to announce that our Extension Paper on domain generalization and clear-to-adverse-weather UDA was accapted at PAMI.
[2023-08-25] We are happy to announce that our follow-up work EDAPS on panoptic segmentation UDA was accepted at ICCV23.
[2023-04-23] We further extend DAFormer to domain generalization and clear-to-adverse-weather UDA in the Extension Paper.
[2023-02-28] We are happy to announce that our follow-up work MIC on context-enhanced UDA was accepted at CVPR23.
[2022-07-06] We are happy to announce that our follow-up work HRDA on high-resolution UDA was accepted at ECCV22.
[2022-03-09] We are happy to announce that DAFormer was accepted at CVPR22.

Overview

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in Unsupervised Domain Adaptation (UDA).

Even though a large number of methods propose new UDA strategies, they are mostly based on outdated network architectures. In this work, we particularly study the influence of the network architecture on UDA performance and propose DAFormer, a network architecture tailored for UDA. It consists of a Transformer encoder and a multi-level context-aware feature fusion decoder.

DAFormer is enabled by three simple but crucial training strategies to stabilize the training and to avoid overfitting the source domain: While the Rare Class Sampling on the source domain improves the quality of pseudo-labels by mitigating the confirmation bias of self-training towards common classes, the Thing-Class ImageNet Feature Distance and a Learning Rate Warmup promote feature transfer from ImageNet pretraining.

DAFormer significantly improves the state-of-the-art performance by 10.8 mIoU for GTA→Cityscapes and by 5.4 mIoU for Synthia→Cityscapes and enables learning even difficult classes such as train, bus, and truck well.

The strengths of DAFormer, compared to the previous state-of-the-art UDA method ProDA, can also be observed in qualitative examples from the Cityscapes validation set.

DAFormer can be further extended to domain generalization lifting the requirement of access to target images. Also in domain generalization, DAFormer significantly improves the state-of-the-art performance by +6.5 mIoU.

For more information on DAFormer, please check our [CVPR Paper] and the [Extension Paper].

If you find this project useful in your research, please consider citing:

@InProceedings{hoyer2022daformer,
  title={{DAFormer}: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation},
  author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={9924--9935},
  year={2022}
}

@Article{hoyer2024domain,
  title={Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation},
  author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)}, 
  year={2024},
  volume={46},
  number={1},
  pages={220-235},
  doi={10.1109/TPAMI.2023.3320613}}
}

Comparison with State-of-the-Art UDA

DAFormer significantly outperforms previous works on several UDA benchmarks. This includes synthetic-to-real adaptation on GTA→Cityscapes and Synthia→Cityscapes as well as clear-to-adverse-weather adaptation on Cityscapes→ACDC and Cityscapes→DarkZurich.

	GTA→CS(val)	Synthia→CS(val)	CS→ACDC(test)	CS→DarkZurich(test)
ADVENT [1]	45.5	41.2	32.7	29.7
BDL [2]	48.5	--	37.7	30.8
FDA [3]	50.5	--	45.7	--
DACS [4]	52.1	48.3	--	--
ProDA [5]	57.5	55.5	--	--
MGCDA [6]	--	--	48.7	42.5
DANNet [7]	--	--	50.0	45.2
DAFormer (Ours)	68.3	60.9	55.4*	53.8*

* New results of our extension paper

References:

Vu et al. "Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation" in CVPR 2019.
Li et al. "Bidirectional learning for domain adaptation of semantic segmentation" in CVPR 2019.
Yang et al. "Fda: Fourier domain adaptation for semantic segmentation" in CVPR 2020.
Tranheden et al. "Dacs: Domain adaptation via crossdomain mixed sampling" in WACV 2021.
Zhang et al. "Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation" in CVPR 2021.
Sakaridis et al. "Map-guided curriculum domain adaptation and uncertaintyaware evaluation for semantic nighttime image segmentation" in TPAMI, 2020.
Wu et al. "DANNet: A one-stage domain adaptation network for unsupervised nighttime semantic segmentation" in CVPR, 2021.

Comparison with State-of-the-Art Domain Generalization (DG)

DAFormer significantly outperforms previous works on domain generalization from GTA to real street scenes.

DG Method	Cityscapes	BDD100K	Mapillary	Avg.
IBN-Net [1,5]	37.37	34.21	36.81	36.13
DRPC [2]	42.53	38.72	38.05	39.77
ISW [3,5]	37.20	33.36	35.57	35.38
SAN-SAW [4]	45.33	41.18	40.77	42.43
SHADE [5]	46.66	43.66	45.50	45.27
DAFormer (Ours)	52.65*	47.89*	54.66*	51.73*

* New results of our extension paper

References:

Pan et al. "Two at once: Enhancing learning and generalization capacities via IBN-Net" in ECCV, 2018.
Yue et al. "Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data" ICCV, 2019.
Choi et al. "RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening" in CVPR, 2021.
Peng et al. "Semantic-aware domain generalized segmentation" in CVPR, 2022.
Zhao et al. "Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation" in ECCV, 2022.

Setup Environment

For this project, we used python 3.8.5. We recommend setting up a new virtual environment:

python -m venv ~/venv/daformer
source ~/venv/daformer/bin/activate

In that environment, the requirements can be installed with:

pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.3.7  # requires the other packages to be installed first

Please, download the MiT ImageNet weights (b3-b5) provided by SegFormer from their OneDrive and put them in the folder pretrained/. Further, download the checkpoint of DAFormer on GTA→Cityscapes and extract it to the folder work_dirs/.

All experiments were executed on an NVIDIA RTX 2080 Ti.

Inference Demo

Already as this point, the provided DAFormer model can be applied to a demo image:

python -m demo.image_demo demo/demo.png work_dirs/211108_1622_gta2cs_daformer_s0_7f24c/211108_1622_gta2cs_daformer_s0_7f24c.json work_dirs/211108_1622_gta2cs_daformer_s0_7f24c/latest.pth

When judging the predictions, please keep in mind that DAFormer had no access to real-world labels during the training.

Setup Datasets

Cityscapes: Please, download leftImg8bit_trainvaltest.zip and gt_trainvaltest.zip from here and extract them to data/cityscapes.

GTA: Please, download all image and label packages from here and extract them to data/gta.

Synthia (Optional): Please, download SYNTHIA-RAND-CITYSCAPES from here and extract it to data/synthia.

ACDC (Optional): Please, download rgb_anon_trainvaltest.zip and gt_trainval.zip from here and extract them to data/acdc. Further, please restructure the folders from condition/split/sequence/ to split/ using the following commands:

rsync -a data/acdc/rgb_anon/*/train/*/* data/acdc/rgb_anon/train/
rsync -a data/acdc/rgb_anon/*/val/*/* data/acdc/rgb_anon/val/
rsync -a data/acdc/gt/*/train/*/*_labelTrainIds.png data/acdc/gt/train/
rsync -a data/acdc/gt/*/val/*/*_labelTrainIds.png data/acdc/gt/val/

Dark Zurich (Optional): Please, download the Dark_Zurich_train_anon.zip and Dark_Zurich_val_anon.zip from here and extract it to data/dark_zurich.

The final folder structure should look like this:

DAFormer
├── ...
├── data
│   ├── acdc (optional)
│   │   ├── gt
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── rgb_anon
│   │   │   ├── train
│   │   │   ├── val
│   ├── cityscapes
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── dark_zurich (optional)
│   │   ├── gt
│   │   │   ├── val
│   │   ├── rgb_anon
│   │   │   ├── train
│   │   │   ├── val
│   ├── gta
│   │   ├── images
│   │   ├── labels
│   ├── synthia (optional)
│   │   ├── RGB
│   │   ├── GT
│   │   │   ├── LABELS
├── ...

Data Preprocessing: Finally, please run the following scripts to convert the label IDs to the train IDs and to generate the class index for RCS:

python tools/convert_datasets/gta.py data/gta --nproc 8
python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8
python tools/convert_datasets/synthia.py data/synthia/ --nproc 8

Training

For convenience, we provide an annotated config file of the final DAFormer. A training job can be launched using:

python run_experiments.py --config configs/daformer/gta2cs_uda_warm_fdthings_rcs_croppl_a999_daformer_mitb5_s0.py

For the experiments in our paper (e.g. network architecture comparison, component ablations, ...), we use a system to automatically generate and train the configs:

python run_experiments.py --exp <ID>

More information about the available experiments and their assigned IDs, can be found in experiments.py. The generated configs will be stored in configs/generated/.

Testing & Predictions

The provided DAFormer checkpoint trained on GTA→Cityscapes (already downloaded by tools/download_checkpoints.sh) can be tested on the Cityscapes validation set using:

sh test.sh work_dirs/211108_1622_gta2cs_daformer_s0_7f24c

The predictions are saved for inspection to work_dirs/211108_1622_gta2cs_daformer_s0_7f24c/preds and the mIoU of the model is printed to the console. The provided checkpoint should achieve 68.85 mIoU. Refer to the end of work_dirs/211108_1622_gta2cs_daformer_s0_7f24c/20211108_164105.log for more information such as the class-wise IoU.

Similarly, also other models can be tested after the training has finished:

sh test.sh path/to/checkpoint_directory

When evaluating a model trained on Synthia→Cityscapes, please note that the evaluation script calculates the mIoU for all 19 Cityscapes classes. However, Synthia contains only labels for 16 of these classes. Therefore, it is a common practice in UDA to report the mIoU for Synthia→Cityscapes only on these 16 classes. As the Iou for the 3 missing classes is 0, you can do the conversion mIoU16 = mIoU19 * 19 / 16.

The results for Cityscapes→ACDC and Cityscapes→DarkZurich are reported on the test split of the target dataset. To generate the predictions for the test set, please run:

python -m tools.test path/to/config_file path/to/checkpoint_file --test-set --format-only --eval-option imgfile_prefix=labelTrainIds to_label_id=False

The predictions can be submitted to the public evaluation server of the respective dataset to obtain the test score.

Domain Generalization

For the domain generalization extension of DAFormer, please refer to the DG branch of the HRDA repository: https://github.com/lhoyer/HRDA/tree/dg

Checkpoints

Below, we provide checkpoints of DAFormer for different benchmarks. As the results in the paper are provided as the mean over three random seeds, we provide the checkpoint with the median validation performance here.

The checkpoints come with the training logs. Please note that:

The logs provide the mIoU for 19 classes. For Synthia→Cityscapes, it is necessary to convert the mIoU to the 16 valid classes. Please, read the section above for converting the mIoU.
The logs provide the mIoU on the validation set. For Cityscapes→ACDC and Cityscapes→DarkZurich the results reported in the paper are calculated on the test split. For DarkZurich, the performance significantly differs between validation and test split. Please, read the section above on how to obtain the test mIoU.

Framework Structure

This project is based on mmsegmentation version 0.16.0. For more information about the framework structure and the config system, please refer to the mmsegmentation documentation and the mmcv documentation.

The most relevant files for DAFormer are:

configs/daformer/gta2cs_uda_warm_fdthings_rcs_croppl_a999_daformer_mitb5_s0.py: Annotated config file for the final DAFormer.
mmseg/models/uda/dacs.py: Implementation of UDA self-training with ImageNet Feature Distance.
mmseg/datasets/uda_dataset.py: Data loader for UDA with Rare Class Sampling.
mmseg/models/decode_heads/daformer_head.py: Implementation of DAFormer decoder with context-aware feature fusion.
mmseg/models/backbones/mix_transformer.py: Implementation of Mix Transformer encoder (MiT).

Acknowledgements

This project is based on the following open-source projects. We thank their authors for making the source code publically available.

License

This project is released under the Apache License 2.0, while some specific features in this repository are with other licenses. Please refer to LICENSES.md for the careful check, if you are using our code for commercial matters.

	def get_rare_class_sample(self):
	c = np.random.choice(self.rcs_classes, p=self.rcs_classprob)
	f1 = np.random.choice(self.samples_with_class[c])
	i1 = self.file_to_idx[f1]
	s1 = self.source[i1]
	if self.rcs_min_crop_ratio > 0:
	for j in range(10):
	n_class = torch.sum(s1['gt_semantic_seg'].data == c)
	# mmcv.print_log(f'{j}: {n_class}', 'mmseg')
	if n_class > self.rcs_min_pixels * self.rcs_min_crop_ratio:
	break
	# Sample a new random crop from source image i1.
	# Please note, that self.source.__getitem__(idx) applies the
	# preprocessing pipeline to the loaded image, which includes
	# RandomCrop, and results in a new crop of the image.
	s1 = self.source[i1]
	i2 = np.random.choice(range(len(self.target)))
	s2 = self.target[i2]

	return {
	**s1, 'target_img_metas': s2['img_metas'],
	'target_img': s2['img']
	}

	if self.cat_max_ratio < 1.:
	# Repeat 10 times
	for _ in range(10):
	seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
	labels, cnt = np.unique(seg_temp, return_counts=True)
	cnt = cnt[labels != self.ignore_index]
	if len(cnt) > 1 and np.max(cnt) / np.sum(
	cnt) < self.cat_max_ratio:
	break
	crop_bbox = self.get_crop_bbox(img)

	target=dict(
	type='CityscapesDataset',
	data_root='data/cityscapes/',
	img_dir='leftImg8bit/train',
	ann_dir='gtFine/train',
	pipeline=cityscapes_train_pipeline)),

lhoyer / daformer Goto Github PK

daformer's Introduction

DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

Overview

Comparison with State-of-the-Art UDA

Comparison with State-of-the-Art Domain Generalization (DG)

Setup Environment

Inference Demo

Setup Datasets

Training

Testing & Predictions

Domain Generalization

Checkpoints

Framework Structure

Acknowledgements

License

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