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Multichannel Semantic Segmentation with Unsupervised Domain Adaptation implemeted by PyTorch

This is the code for the paper (Multichannel Semantic Segmentation with Unsupervised Domain Adaptation) in AutoNUE workshop at ECCV-2018.

Setting

setting

Sample results

result

If you find this code(sorry still too messy) useful in your research, please consider citing:

@inproceedings{watanabe2018multichannel,
  title={Multichannel Semantic Segmentation with Unsupervised Domain Adaptation},
  author={Watanabe, Kohei and Saito, Kuniaki and Ushiku, Yoshitaka and Harada, Tatsuya},
  booktitle={Proceedings of the on AUTONUE Workshops of ECCV 2018},
  year={2018},
  organization={Springer}
}

Installation

Use Python 2.x

First, you need to install PyTorch following the official site instruction.

Next, please install the required libraries as follows;

pip install -r requirements.txt

Dataset Preparation

Please download datasets from URLs below;

Then, edit the get_dataset function in datasets.py.

Demo

demo

You can try our demo online

First, download the trained model as follows;

wget https://www.dropbox.com/s/4lis0cjju5ounlg/dual_model.tar

Then, run the demo script as follows;

python demo.py  sample_img/rgb_5947.png dual_model.tar

Result will be saved under demo_output directory.

Usage

We adopted Maximum Classifier Discrepancy (MCD) for unsupervised domain adaptation.

MCD Training

  • adapt_xxx.py

    • for domain adaptation (MCD)

  • dann_xxx.py

    • for domain adaptation (DANN: Domain Adversarial Neural Network)

  • source_xxx.py

    • for source only

Fusion-based approach

Early Fusion

python adapt_trainer.py suncg nyu --input_ch 6 --src_split train_rgbhhab --tgt_split trainval_rgbhha

Late Fusion

python adapt_mfnet_trainer.py suncg nyu --input_ch 6 --src_split train_rgbhhab --tgt_split trainval_rgbhha --method_detail MFNet-AddFusion

Score Fusion

python adapt_mfnet_trainer.py suncg nyu --input_ch 6 --src_split train_rgbhhab --tgt_split trainval_rgbhha --method_detail MFNet-ScoreAddFusion

Multitask learning approach

Segmentation + Depth Estimation (HHA regression)

python adapt_multitask_trainer.py suncg nyu --input_ch 6 --src_split train_rgbhhab --tgt_split trainval_rgbhha --method_detail MFNet-ScoreAddFusion

Segmentation + Depth Estimation (HHA regression) + Boundary Detection

python adapt_tripletask_trainer.py suncg nyu --input_ch 6 --src_split train_rgbhhab --tgt_split trainval_rgbhha  --method_detail MFNet-ScoreAddFusion

Test

For dual task,

python adapt_multitask_tester.py nyu --split test_rgbhha train_output/suncg-train_rgbhha2nyu-trainval_rgbhha_6ch_MCDmultitask/pth/MCD-normal-drn_d_38-20.pth.tar

For triple task,

python adapt_triple_multitask_tester.py nyu --split test_rgbhha train_output/suncg-train_rgbhhab2nyu-trainval_rgbhha_6ch_MCD_triple_multitask/pth/MCD-normal-drn_d_38-10.pth.tar

Results will be saved under "./test_output/suncg-train_rgbhhab2nyu-trainval_rgbhha_6ch_MCD_triple_multitask---nyu-test_rgbhha/MCD-normal-drn_d_38-10.tar/" .

Postprocess using Boundary Detection output

You need Matlab.

bash ./sample_scripts/refine_seg_by_boundary.sh

Evaluation

python eval.py nyu ./test_output/suncg-train_rgbhhab2nyu-trainval_rgbhha_6ch_MCD_triple_multitask---nyu-test_rgbhha/YOUR_MODEL_NAME/label

Referenced codes