Home

Awesome

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). <img src="data/pipeline.png" width="1000">

Results

<img src="data/results.png" width="600">

Environment

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.

Datasets

Four datasets are supported:

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/.

Methods

Commands

Office-31

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'

Office-Home

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'

PACS

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'

DomainNet

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'

Citation

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}
}