This repository contains PyTorch implementation code for awesome continual learning method DualPrompt,
Wang, Zifeng, et al. "DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning." ECCV. 2022.
The official Jax implementation is here.
The system I used and tested in
- Ubuntu 20.04.4 LTS
- Slurm 21.08.1
- NVIDIA GeForce RTX 3090
- Python 3.8
First, clone the repository locally:
git clone https://github.com/JH-LEE-KR/dualprompt-pytorch
cd dualprompt-pytorch
Then, install the packages below:
pytorch==1.12.1
torchvision==0.13.1
timm==0.6.7
pillow==9.2.0
matplotlib==3.5.3
These packages can be installed easily by
pip install -r requirements.txt
If you already have CIFAR-100 or ImageNet-R, pass your dataset path to --data-path
.
The datasets aren't ready, change the download argument in datasets.py
as follows
CIFAR-100
datasets.CIFAR100(download=True)
ImageNet-R
Imagenet_R(download=True)
To train a model via command line:
Single node with single gpu
python -m torch.distributed.launch \
--nproc_per_node=1 \
--use_env main.py \
<cifar100_dualprompt or imr_dualprompt> \
--model vit_base_patch16_224 \
--batch-size 24 \
--data-path /local_datasets/ \
--output_dir ./output
Single node with multi gpus
python -m torch.distributed.launch \
--nproc_per_node=<Num GPUs> \
--use_env main.py \
<cifar100_dualprompt or imr_dualprompt> \
--model vit_base_patch16_224 \
--batch-size 24 \
--data-path /local_datasets/ \
--output_dir ./output
Also available in Slurm system by changing options on train_cifar100_dualprompt.sh
or train_imr_dualprompt.sh
properly.
Distributed training is available via Slurm and submitit:
pip install submitit
To train a model on 2 nodes with 4 gpus each:
python run_with_submitit.py <cifar100_dualprompt or imr_dualprompt> --shared_folder <Absolute Path of shared folder for all nodes>
Absolute Path of shared folder must be accessible from all nodes.
According to your environment, you can use NCLL_SOCKET_IFNAME=<Your own IP interface to use for communication>
optionally.
To evaluate a trained model:
python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py <cifar100_dualprompt or imr_dualprompt> --eval
Test results on a single gpu.
Name | Acc@1 | Forgetting |
---|---|---|
Pytorch-Implementation | 86.13 | 5.17 |
Reproduce Official-Implementation | 85.59 | 5.03 |
Paper Results | 86.51 | 5.16 |
Name | Acc@1 | Forgetting |
---|---|---|
Pytorch-Implementation | 68.23 | 4.49 |
Reproduce Official-Implementation | 67.55 | 5.06 |
Paper Results | 68.13 | 4.68 |
Here are the metrics used in the test, and their corresponding meanings:
Metric | Description |
---|---|
Acc@1 | Average evaluation accuracy up until the last task |
Forgetting | Average forgetting up until the last task |
This repository is released under the Apache 2.0 license as found in the LICENSE file.
@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={European Conference on Computer Vision},
year={2022}
}