tpekarekrosin/dualprompt-pytorch

PyTorch Implementation of DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning @ ECCV22

DualPrompt PyTorch Implementation

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.

Environment

The system I used and tested in

• Ubuntu 20.04.4 LTS
• Slurm 21.08.1
• NVIDIA GeForce RTX 3090
• Python 3.8

Usage

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

Data preparation

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)

Training

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.

Multinode train

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.

Evaluation

To evaluate a trained model:

python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py <cifar100_dualprompt or imr_dualprompt> --eval

Result

Test results on a single gpu.

Split-CIFAR100

Name	Acc@1	Forgetting
Pytorch-Implementation	86.13	5.17
Reproduce Official-Implementation	85.59	5.03
Paper Results	86.51	5.16

Split-ImageNet-R

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

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

Cite

@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}
}