Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation

This repository includes the PyTorch implementation for the paper Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation.

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File Structure

weights/ ├── weights/ | ├── synthia/ | ├── gta5/ | | ├── gta5_ours_drn_57.98.pth | | ├── ... Rethinking_EnD_UDA/ ├── label_fusion/ ├── train_deeplabv2/ ├── train_deeplabv3+/ ├── ... Warehouse/ ├── SYNTHIA/ │ ├── labels/ │ ├── images/ | | ├── 0000000.png | | ├── 0000001.png | | ├── ... ├── GTA5/ │ ├── image/ │ ├── labels/ | | ├── 00000.png | | ├── 00001.png | | ├── ... ├── Cityscapes/ │ ├── data/ │ │ ├── gtFine/ │ │ ├── leftImg8bit/ │ │ │ ├── train/ │ | | ├── val/ │ | | ├── test/ │ │ | | ├── aachen │ │ | | ├── ...

Training

Quick Start: 1. Download the pre-generated pseudo labels here. 2. Place the pseudo labels in Cityscapes/data/gtFine folder and train the model with the following commands: cd train_deeplabv3+ python train.py --class-balance --often-balance --backbone drn --restore-from ../../weights/weights/gta5/source/model_34.80.pth

The whole training procedure: 1. Train the teacher models - DACS - CRST - CBST - R-MRNet 2. Generate the pseudo labels and the output tensors. (NOTE: it is recommended that the certainty tensors should be first mapped to 0~100 and stored using byte tensors for memory conservation.)

1. Fuse the pseudo labels cd label_fusion python3 label_fusion.py
2. Place the pseudo labels in Cityscapes/data/gtFine folder and follow the instructions in "Quick Start" to train the model.

Testing

``` ================ GTA5 ================ { Deeplabv3+ } cd traindeeplabv3+ python test.py --backbone drn --restore-from ../../weights/weights/gta5/gta5oursdrn57.98.pth

============== SYNTHIA =============== { Deeplabv3+ } cd traindeeplabv3+ python test.py --num-classes 16 --source-domain synthia --backbone drn --restore-from ../../weights/weights/synthia/synthiaoursdrn59.95.pth ```

Pretrained Weights

You can download the pre-trained weights here.

Prerequisites

• Python 3.6
• Pytorch 1.5.0

Download the dependencies: pip install requirement.txt

Reference

If you find the code useful for your research, please consider citing @InProceedings{Chao_2021_CVPR, author = {Chao, Chen-Hao and Cheng, Bo-Wun and Lee, Chun-Yi}, title = {Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2610-2620} }

Acknowledgement

The code is partially borrowed from the following works: - R-MRNet: https://github.com/layumi/Seg-Uncertainty - Deeplabv3+: https://github.com/jfzhang95/pytorch-deeplab-xception