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codebase update, including more benchmarks and DualPrompt
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2 changes: 1 addition & 1 deletion CONTRIBUTING.md
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How to Contribute
SimCLR needs to maintain permanent compatibility with the pre-trained model files, so we do not plan to make any major changes to this library (other than what was promised in the README). However, we can accept small patches related to re-factoring and documentation. To submit contributes, there are just a few small guidelines you need to follow.
L2P/DualPrompt needs to maintain permanent compatibility with the pre-trained model files, so we do not plan to make any major changes to this library (other than what was promised in the README). However, we can accept small patches related to re-factoring and documentation. To submit contributes, there are just a few small guidelines you need to follow.

Contributor License Agreement
Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project. Head over to https://cla.developers.google.com/ to see your current agreements on file or to sign a new one.
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34 changes: 1 addition & 33 deletions LICENSE
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61 changes: 45 additions & 16 deletions README.md
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# [Learning to Prompt for Continual Learning (L2P)](https://arxiv.org/pdf/2112.08654.pdf) Official Jax Implementation
# [Learning to Prompt for Continual Learning (L2P)](https://arxiv.org/pdf/2112.08654.pdf) and [DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning](https://arxiv.org/pdf/2204.04799.pdf) Official Jax Implementation

This codebase contains the implementation of two continual learning methods: **L2P (CVPR2022)** and **DualPrompt (ECCV2022)**.

## Introduction
L2P is a novel continual learning technique which learns to dynamically prompt a pre-trained model to learn tasks sequentially under different task transitions. Different from mainstream rehearsal-based or architecture-based methods, L2P requires neither a rehearsal buffer nor test-time task identity. L2P can be generalized to various continual learning settings including the most challenging and realistic task-agnostic setting. L2P consistently outperforms prior state-of-the-art methods. Surprisingly, L2P achieves competitive results against rehearsal-based methods even without a rehearsal buffer.
<p align="center">
<img src="./l2p_illustration.png" width="850" height="320">
</p>
DualPrompt improves upon L2P by attaching complementary prompts to the pre-trained backbone, and then formulates the objective as learning task-invariant and task-specific “instructions". With extensive experimental validation, DualPrompt consistently sets state-of-the-art performance under the challenging class-incremental setting. In particular, DualPrompt outperforms recent advanced continual learning methods with relatively large buffer sizes. We also introduce a more challenging benchmark, Split ImageNet-R, to help generalize rehearsal-free continual learning research.
<p align="center">
<img src="./dualprompt_illustration.png" width="850" height="320">
</p>


Code is written by Zifeng Wang. Acknowledgement to https://github.com/google-research/nested-transformer.

This is not an officially supported Google product.
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pip install -r requirements.txt
```
After this, you may need to adjust your jax version according to your cuda driver version so that jax correctly identifies your GPUs (see [this issue](https://github.com/google/jax/issues/5231) for more details).
For example, if your cuda version is 11.1, you need to run the following:
```
pip install --upgrade jax==0.2.14 jaxlib==0.1.67+cuda111 -f https://storage.googleapis.com/jax-releases/jax_releases.html
```

Note: The codebase has been throughly tested under the TPU enviroment using the newest JAX version. We are currently working on verifying the GPU environment.

## Dataset preparation
Before running experiments for 5-datasets and CORe50, additional dataset preparation step should be conducted as follows:

1. Download CORe50 classification benchmark here: https://vlomonaco.github.io/core50/ and download not-mnist here: http://yaroslavvb.com/upload/notMNIST/
2. Transform them into TFDS compatible form following the tutorial in https://www.tensorflow.org/datasets/add_dataset
3. Replace corresponding dataset paths `"PATH_TO_CORE50"` and `"PATH_TO_NOT_MNIST"` in `input_pipeline.py` by the destination paths in step 2


## Getting pretrained ViT model
ViT-B/16 model used in this paper can be downloaded at https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz.
ViT-B/16 model used in this paper can be downloaded at [here](https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz).
Note: Our codebase actually supports various sizes of ViTs. If you would like to try variations of ViTs, feel free to change the `config.model_name` in the config files, following the valid options defined in [vit.py](models/vit.py).


## Instructions on running L2P
We provide the configuration file to train and evaluate L2P on multiple benchmarks in `configs`.
We provide the configuration file to train and evaluate L2P and DualPrompt on multiple benchmarks in [configs](configs/).


To run our method on the Split CIFAR-100 dataset (class-incremental setting):
To run L2P on benchmark datasets:

```
python main.py --my_config configs/cifar100_l2p.py --workdir=./cifar100_l2p --my_config.init_checkpoint=<ViT-saved-path/ViT-B_16.npz>
python main.py --my_config configs/$L2P_CONFIG --workdir=./l2p --my_config.init_checkpoint=<ViT-saved-path/ViT-B_16.npz>
```
where `$L2P_CONFIG` can be one of the followings: `[cifar100_l2p.py, five_datasets_l2p.py, core50_l2p.py, cifar100_gaussian_l2p.py]`.

Note: we run our experiments using 8 V100 GPUs or 4 TPUs, and we specify a per device batch size of 16 in the config files. This indicates that we use a total batch size of 128.

To run our method on the more complex Gaussian Scheduled CIFAR-100 dataset (task-agnostic setting):

To run DualPrompt on benchmark datasets:

```
python main.py --my_config configs/cifar100_gaussian_l2p.py --workdir=./cifar100_gaussian_l2p --my_config.init_checkpoint=<ViT-saved-path/ViT-B_16.npz>
python main.py --my_config configs/$DUALPROMPT_CONFIG --workdir=./dualprompt --my_config.init_checkpoint=<ViT-saved-path/ViT-B_16.npz>
```
where `$DUALPROMPT_CONFIG` can be one of the followings: `[imr_dualprompt.py, cifar100_dualprompt.py]`.


Note: we run our experiments using 8 V100 GPUs or 4 TPUs, and we specify a per device batch size of 16 in the config files. This indicates that we use a total batch size of 128.


## Visualize results
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## Cite
```
@inproceedings{wang2021learning,
title={Learning to Prompt for Continual Learning},
author={Zifeng Wang and Zizhao Zhang and Chen-Yu Lee and Han Zhang and Ruoxi Sun and Xiaoqi Ren and Guolong Su and Vincent Perot and Jennifer Dy and Tomas Pfister},
booktitle={CVPR},
@inproceedings{wang2022learning,
title={Learning to prompt for continual learning},
author={Wang, Zifeng and Zhang, Zizhao and Lee, Chen-Yu and Zhang, Han and Sun, Ruoxi and Ren, Xiaoqi and Su, Guolong and Perot, Vincent and Dy, Jennifer and Pfister, Tomas},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={139--149},
year={2022}
}
@article{wang2022dualprompt,
title={DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning},
author={Wang, Zifeng and Zhang, Zizhao and Ebrahimi, Sayna and Sun, Ruoxi and Zhang, Han and Lee, Chen-Yu and Ren, Xiaoqi and Su, Guolong and Perot, Vincent and Dy, Jennifer and others},
journal={arXiv preprint arXiv:2204.04799},
year={2022}
}
```
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