PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)

Overview

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).

Results

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

  • CDAN
  • CDAN+E

Commands

Office-31

python src/main.py \
        --method 'CDAN' \
        --encoder 'ResNet50' \
 	--dataset 'office31' \
 	--data_root [your office31 folder] \
 	--source 'dslr' \
 	--target 'webcam' 'amazon' \
 	--source_iters 200 \
 	--adapt_iters 3000 \
 	--finetune_iters 15000 \
 	--lambda_node 0.3 \
 	--output_dir 'office31-dcgct/dslr_rest/CDAN'

Office-Home

python src/main.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' 

PACS

python src/main.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'  

DomainNet

python src/main.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 0.1 \
	--output_dir 'domainNet-dcgct/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}
}
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Comments
  • About

    About "upgrade_source_domain"

    Hi, thanks for your inspiring work! I have read the code and have some doubt about it. During the curriculum learning, why not remove the pseudo labelled target samples from the target domain after they are added to the source domain? Expect your reply. Thanks.

    opened by liujianzhao6328057 6
  • DomainNet: Accuracy Information

    DomainNet: Accuracy Information

    Wanted to know that whether the accuracy reported in the paper is achieved through test split or train split.

    |Train | clipart_train.txt | infograph_train.txt | painting_train.txt | quickdraw_train.txt | real_train.txt | sketch_train.txt| -- | -- | -- | -- | -- | -- | -- | Test | clipart_test.txt | infograph_test.txt | painting_test.txt | quickdraw_test.txt | real_test.txt | sketch_test.txt|

    Thanks.

    opened by take2rohit 2
  • How to run CGCT instead of D-CGCT?

    How to run CGCT instead of D-CGCT?

    From my understanding this code by default runs the D-CGCT variant of your method. However I am interested in comparing its performances with a MTDA method I am working on, which does not use domain labels for target samples. What should I do to run D-CGCT? Should I simply put all target data in a single txt file and use it as if it was a single target?

    Thanks in advance

    opened by FrancescoCappio 2
  • source pretrained models

    source pretrained models

    Hi, Thank you for the awesome work! Can you provide the source pretrained model or any command to reproduce your result in Table 4 row 1 (w/o target source train) result?

    opened by lzhangbj 0
Owner
Evgeny
Evgeny
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