https://github.com/tim-learn/SHOT
code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"
https://github.com/tim-learn/SHOT
domain-adaptation icml-2020 multi-source-domain-adaptation partial-set-domain-adaptation pseudo-labeling source-free-domain-adaptation source-hypothesis-transfer transfer-learning
Last synced: 5 months ago
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code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"
- Host: GitHub
- URL: https://github.com/tim-learn/SHOT
- Owner: tim-learn
- License: mit
- Created: 2020-06-14T13:21:36.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-02-22T08:44:47.000Z (over 1 year ago)
- Last Synced: 2024-11-15T06:32:05.911Z (11 months ago)
- Topics: domain-adaptation, icml-2020, multi-source-domain-adaptation, partial-set-domain-adaptation, pseudo-labeling, source-free-domain-adaptation, source-hypothesis-transfer, transfer-learning
- Language: Python
- Homepage:
- Size: 503 KB
- Stars: 436
- Watchers: 7
- Forks: 78
- Open Issues: 4
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# Official implementation for **SHOT**
## [**[ICML-2020] Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation**](http://proceedings.mlr.press/v119/liang20a.html)
- **2022/6/6 We correct a bug in the pseudo-labeling function (def obtain_label), many thanks to @TomSheng21.**
- **2022/2/8 We upload the pretrained source models via Google drive in [pretrained-models.md](./pretrained-models.md).**
### Attention-v2: ***we release the code of our recent black-box UDA method (DINE, https://arxiv.org/pdf/2104.01539.pdf) in the following repository (https://github.com/tim-learn/DINE).***
#### Attention: ***The code of our stronger TPAMI extension (SHOT++, https://arxiv.org/pdf/2012.07297.pdf) has been released in a new repository (https://github.com/tim-learn/SHOT-plus).***
### Results:
#### **Note that we update the code and further consider the standard learning rate scheduler like DANN and report new results in the final camera ready version.** Please refer [results.md](./results.md) for the detailed results on various datasets.
*We have updated the results for **Digits**. Now the results of SHOT-IM for **Digits** are stable and promising. (Thanks to @wengzejia1 for pointing the bugs in **uda_digit.py**).*
### Framework:
### Prerequisites:
- python == 3.6.8
- pytorch ==1.1.0
- torchvision == 0.3.0
- numpy, scipy, sklearn, PIL, argparse, tqdm
### Dataset:
- Please manually download the datasets [Office](https://drive.google.com/file/d/0B4IapRTv9pJ1WGZVd1VDMmhwdlE/view), [Office-Home](https://drive.google.com/file/d/0B81rNlvomiwed0V1YUxQdC1uOTg/view), [VisDA-C](https://github.com/VisionLearningGroup/taskcv-2017-public/tree/master/classification), [Office-Caltech](http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar) from the official websites, and modify the path of images in each '.txt' under the folder './object/data/'. [**How to generate such txt files could be found in https://github.com/tim-learn/Generate_list **]
- Concerning the **Digits** dsatasets, the code will automatically download three digit datasets (i.e., MNIST, USPS, and SVHN) in './digit/data/'.
### Training:
1. ##### Unsupervised Closed-set Domain Adaptation (UDA) on the Digits dataset
- MNIST -> USPS (**m2u**) SHOT (**cls_par = 0.1**) and SHOT-IM (**cls_par = 0.0**)
```python
cd digit/
python uda_digit.py --dset m2u --gpu_id 0 --output ckps_digits --cls_par 0.0
python uda_digit.py --dset m2u --gpu_id 0 --output ckps_digits --cls_par 0.1
```
2. ##### Unsupervised Closed-set Domain Adaptation (UDA) on the Office/ Office-Home dataset
- Train model on the source domain **A** (**s = 0**)
```python
cd object/
python image_source.py --trte val --da uda --output ckps/source/ --gpu_id 0 --dset office --max_epoch 100 --s 0
```
- Adaptation to other target domains **D and W**, respectively
```python
python image_target.py --cls_par 0.3 --da uda --output_src ckps/source/ --output ckps/target/ --gpu_id 0 --dset office --s 0
```
3. ##### Unsupervised Closed-set Domain Adaptation (UDA) on the VisDA-C dataset
- Synthetic-to-real
```python
cd object/
python image_source.py --trte val --output ckps/source/ --da uda --gpu_id 0 --dset VISDA-C --net resnet101 --lr 1e-3 --max_epoch 10 --s 0
python image_target.py --cls_par 0.3 --da uda --dset VISDA-C --gpu_id 0 --s 0 --output_src ckps/source/ --output ckps/target/ --net resnet101 --lr 1e-3
```
4. ##### Unsupervised Partial-set Domain Adaptation (PDA) on the Office-Home dataset
- Train model on the source domain **A** (**s = 0**)
```python
cd object/
python image_source.py --trte val --da pda --output ckps/source/ --gpu_id 0 --dset office-home --max_epoch 50 --s 0
```
- Adaptation to other target domains **C and P and R**, respectively
```python
python image_target.py --cls_par 0.3 --threshold 10 --da pda --dset office-home --gpu_id 0 --s 0 --output_src ckps/source/ --output ckps/target/
```
5. ##### Unsupervised Open-set Domain Adaptation (ODA) on the Office-Home dataset
- Train model on the source domain **A** (**s = 0**)
```python
cd object/
python image_source.py --trte val --da oda --output ckps/source/ --gpu_id 0 --dset office-home --max_epoch 50 --s 0
```
- Adaptation to other target domains **C and P and R**, respectively
```python
python image_target_oda.py --cls_par 0.3 --da oda --dset office-home --gpu_id 0 --s 0 --output_src ckps/source/ --output ckps/target/
```
6. ##### Unsupervised Multi-source Domain Adaptation (MSDA) on the Office-Caltech dataset
- Train model on the source domains **A** (**s = 0**), **C** (**s = 1**), **D** (**s = 2**), respectively
```python
cd object/
python image_source.py --trte val --da uda --output ckps/source/ --gpu_id 0 --dset office-caltech --max_epoch 100 --s 0
python image_source.py --trte val --da uda --output ckps/source/ --gpu_id 0 --dset office-caltech --max_epoch 100 --s 1
python image_source.py --trte val --da uda --output ckps/source/ --gpu_id 0 --dset office-caltech --max_epoch 100 --s 2
```
- Adaptation to the target domain **W** (**t = 3**)
```python
python image_target.py --cls_par 0.3 --da uda --output_src ckps/source/ --output ckps/target/ --gpu_id 0 --dset office --s 0
python image_target.py --cls_par 0.3 --da uda --output_src ckps/source/ --output ckps/target/ --gpu_id 0 --dset office --s 1
python image_target.py --cls_par 0.3 --da uda --output_src ckps/source/ --output ckps/target/ --gpu_id 0 --dset office --s 2
python image_multisource.py --cls_par 0.0 --da uda --dset office-caltech --gpu_id 0 --t 3 --output_src ckps/source/ --output ckps/target/
```
7. ##### Unsupervised Multi-target Domain Adaptation (MTDA) on the Office-Caltech dataset
- Train model on the source domain **A** (**s = 0**)
```python
cd object/
python image_source.py --trte val --da uda --output ckps/source/ --gpu_id 0 --dset office-caltech --max_epoch 100 --s 0
```
- Adaptation to multiple target domains **C and P and R** at the same time
```python
python image_multitarget.py --cls_par 0.3 --da uda --dset office-caltech --gpu_id 0 --s 0 --output_src ckps/source/ --output ckps/target/
```
8. ##### Unsupervised Partial Domain Adaptation (PDA) on the ImageNet-Caltech dataset without source training by ourselves (using the downloaded Pytorch ResNet50 model directly)
- ImageNet -> Caltech (84 classes) [following the protocol in [PADA](https://github.com/thuml/PADA/tree/master/pytorch/data/imagenet-caltech)]
```python
cd object/
python image_pretrained.py --gpu_id 0 --output ckps/target/ --cls_par 0.3
```
**Please refer *./object/run.sh*** for all the settings for different methods and scenarios.
### Citation
If you find this code useful for your research, please cite our papers
```
@inproceedings{liang2020we,
title={Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation},
author={Liang, Jian and Hu, Dapeng and Feng, Jiashi},
booktitle={International Conference on Machine Learning (ICML)},
pages={6028--6039},
year={2020}
}
@article{liang2021source,
title={Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer},
author={Liang, Jian and Hu, Dapeng and Wang, Yunbo and He, Ran and Feng, Jiashi},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year={2021},
note={In Press}
}
```
### Contact
- [liangjian92@gmail.com](mailto:liangjian92@gmail.com)
- [dapeng.hu@u.nus.edu](mailto:dapeng.hu@u.nus.edu)
- [elefjia@nus.edu.sg](mailto:elefjia@nus.edu.sg)