PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)
This repo presents PyTorch implementation of Multi-targe Graph Domain Adaptation framework from "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" CVPR 2021.
The framework is pivoted around two key concepts: graph feature aggregation and curriculum learning (see pipeline below or project web-page).
Python >= 3.6
PyTorch >= 1.8.1
To install dependencies run (line 1 for pip or line 2 for conda env):
pip install -r requirements.txt
conda install --file requirements.txt
Disclaimer. This code has been tested with cuda toolkit 10.2. Please install PyTorch as supported by your machine.
Four datasets are supported:
- Office-31 (Kate Saenko et al., 2010)
- Office-Home (Venkateswara et al., 2017)
- PACS (Li et al., 2017)
- DomainNet (Peng et al., 2019)
To run this code, one must check if the txt file names in data/<dataset_name> are matching with the downloaded domain folders. For e.g., to run OfficeHome, the domain sub-folders should be art/, clipart/, product/ and real/ corresponding to art.txt, clipart.txt, product.txt and real.txt that can be found in the data/office-home/.
- CDAN
- CDAN+E
Run D-CGCT:
python src/main_dcgct.py \
--method 'CDAN' \
--encoder 'ResNet50' \
--dataset 'office31' \
--data_root [your office31 folder] \
--source 'webcam' \
--target 'dslr' 'amazon' \
--source_iters 200 \
--adapt_iters 3000 \
--finetune_iters 15000 \
--lambda_node 0.3 \
--output_dir 'office31-dcgct/webcam_rest/CDAN'
Run CGCT:
python src/main_cgct.py \
--method 'CDAN' \
--encoder 'ResNet50' \
--dataset 'office31' \
--data_root [your office31 folder] \
--source 'webcam' \
--target 'dslr' 'amazon' \
--source_iters 100 \
--adapt_iters 3000 \
--finetune_iters 15000 \
--lambda_node 0.1 \
--output_dir 'office31-cgct/webcam_rest/CDAN'
python src/main_dcgct.py \
--method 'CDAN' \
--encoder 'ResNet50' \
--dataset 'office-home' \
--data_root [your OfficeHome folder] \
--source 'art' \
--target 'clipart' 'product' 'real' \
--source_iters 500 \
--adapt_iters 10000 \
--finetune_iters 15000 \
--lambda_node 0.3 \
--output_dir 'officeHome-dcgct/art_rest/CDAN'
python src/main_cgct.py \
--method 'CDAN' \
--encoder 'ResNet50' \
--dataset 'office-home' \
--data_root [your OfficeHome folder] \
--source 'art' \
--target 'clipart' 'product' 'real' \
--source_iters 500 \
--adapt_iters 5000 \
--finetune_iters 15000 \
--lambda_node 0.1 \
--output_dir 'officeHome-cgct/art_rest/CDAN'
python src/main_dcgct.py \
--method 'CDAN' \
--encoder 'ResNet50' \
--dataset 'pacs' \
--data_root [your PACS folder] \
--source 'photo' \
--target 'cartoon' 'art_painting' 'sketch' \
--source_iters 200 \
--adapt_iters 3000 \
--finetune_iters 15000 \
--lambda_node 0.1 \
--output_dir 'pacs-dcgct/photo_rest/CDAN'
python src/main_cgct.py \
--method 'CDAN' \
--encoder 'ResNet50' \
--dataset 'pacs' \
--data_root [your PACS folder] \
--source 'photo' \
--target 'cartoon' 'art_painting' 'sketch' \
--source_iters 200 \
--adapt_iters 3000 \
--finetune_iters 15000 \
--lambda_node 0.1 \
--output_dir 'pacs-cgct/photo_rest/CDAN'
python src/main_dcgct.py \
--method 'CDAN' \
--encoder 'ResNet101' \
--dataset 'domain-net' \
--data_root [your DomainNet folder] \
--source 'sketch' \
--target 'clipart' 'infograph' 'painting' 'real' 'quickdraw' \
--source_iters 5000 \
--adapt_iters 50000 \
--finetune_iters 15000 \
--lambda_node 0.3 \
--output_dir 'domainNet-dcgct/sketch_rest/CDAN'
python src/main_cgct.py \
--method 'CDAN' \
--encoder 'ResNet101' \
--dataset 'domain-net' \
--data_root [your DomainNet folder] \
--source 'sketch' \
--target 'clipart' 'infograph' 'painting' 'real' 'quickdraw' \
--source_iters 5000 \
--adapt_iters 50000 \
--finetune_iters 15000 \
--lambda_node 0.3 \
--output_dir 'domainNet-cgct/sketch_rest/CDAN'
If you find our paper and code useful for your research, please consider citing our paper.
@inproceedings{roy2021curriculum,
title={Curriculum Graph Co-Teaching for Multi-target Domain Adaptation},
author={Roy, Subhankar and Krivosheev, Evgeny and Zhong, Zhun and Sebe, Nicu and Ricci, Elisa},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2021}
}
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