L2P PyTorch Implementation

This repository contains PyTorch implementation code for awesome continual learning method L2P,
Wang, Zifeng, et al. "Learning to prompt for continual learning." CVPR. 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/l2p-pytorch
cd l2p-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 5-Datasets (MNIST, Fashion-MNIST, NotMNIST, CIFAR10, SVHN), 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)

5-Datasets

datasets.CIFAR10(download=True)
MNIST_RGB(download=True)
FashionMNIST(download=True)
NotMNIST(download=True)
SVHN(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_l2p or five_datasets_l2p> \
        --model vit_base_patch16_224 \
        --batch-size 16 \
        --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_l2p or five_datasets_l2p> \
        --model vit_base_patch16_224 \
        --batch-size 16 \
        --data-path /local_datasets/ \
        --output_dir ./output 

Also available in Slurm system by changing options on train_cifar100_l2p.sh or train_five_datasets.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_l2p or five_datasets_l2p> --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_l2p or five_datasets_l2p> --eval

Result

Test results on a single gpu.

Split-CIFAR100

Name	Acc@1	Forgetting
Pytorch-Implementation	83.77	6.63
Reproduce Official-Implementation	82.59	7.88
Paper Results	83.83	7.63

5-Datasets

Name	Acc@1	Forgetting
Pytorch-Implementation	80.22	3.81
Reproduce Official-Implementation	79.68	3.71
Paper Results	81.14	4.64

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

@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}
}