Overview

This is the official repository of A Theoretical Study on Solving Continual Learning (NeurIPS 2022).

In this repository, we provide the codes for Sup+CSI and Sup+CSI+c. For HAT+CSI and HAT+CSI+c, please check the repository of our workshop paper (Link)

Note

Check out our related paper

A Multi-Head Model for Continual Learning via Out-of-Distribution Replay, CoLLAs 2022 [PDF]

Environments

The code has been tested on the machine with

RTX 3090

cuda=11.4
pytorch=1.7.1
torchvision=0.8.2
cudatoolkit=11.0.221
tensorboardx=2.1
diffdist=0.1
gdown=4.4.0

Please install the necessary packages

Training

Please change --dataset --config --log_dir --name for different experiments

python experiments/GG/run.py --data='./data' --dataset 'cifar10' --config 'cifar10_5t_csi' --log_dir './logs/cifar10_5t_csi' --name 'cifar10_5t_csi' --ood_method 'csi' --round 0 --gpu-sets=0 --seeds 1 --save --original --amp

choices of dataset: mnist, cifar10, cifar100, timgnet choices of config: mnist_5t_csi, cifar10_5t_csi, cifar100_10t_csi, cifar100_20t_csi, timgnet_5t_csi, timgnet_10t_csi

Without mixed precision, remove --amp

To resume training,

python experiments/GG/run.py --data './data' --dataset 'cifar10' --config 'cifar10_5t_csi' --log_dir './logs/cifar10_5t_csi' --load_path './logs/cifar10_5t_csi/cifar10_5t_csi_round_0/result_joint_3.pt' --name 'cifar10_5t_csi' --ood_method 'csi' --round 0 --gpu-sets=0 --seeds 1 --resume_task 4 --original

Please change load_path and resume_task for your experiment.

Training calibration parameters

Please change --dataset, --config --log_dir, --load_path, --name, --calibration_task

python experiments/GG/run.py --data './data' --dataset 'cifar10' --config 'cifar10_5t_csi' --log_dir './logs/cifar10_5t_csi' --load_path './logs/cifar10_5t_csi/cifar10_5t_csi_round_0/result_joint_4.pt' --name 'cifar10_5t_csi' --print_filename 'calibration_4.txt' --ood_method 'csi' --round 0 --gpu-sets=0 --seeds 1 --original --calibration_task 4

The numbers for --calibration_task and result_joint_NUMBER.pt in --load_path must be the same

Evaluation without calibration

python experiments/GG/run.py --data './data' --dataset 'cifar10' --config 'cifar10_5t_csi' --log_dir './logs/cifar10_5t_csi' --load_path './logs/cifar10_5t_csi/cifar10_5t_csi_round_0/result_joint_4.pt' --name 'cifar10_5t_csi' --print_filename 'eval_without_cal.txt' --round 0 --gpu-sets=0 --seeds 1 --ood_method 'csi' --original

Evaluation using calibration

python experiments/GG/run.py --data './data' --dataset 'cifar10' --config 'cifar10_5t_csi' --log_dir './logs/cifar10_5t_csi' --load_path './logs/cifar10_5t_csi/cifar10_5t_csi_round_0/result_joint_4.pt' --name 'cifar10_5t_csi' --cal_pretrain './logs/cifar10_5t_csi/cifar10_5t_csi_round_0/calibration_4' --print_filename 'eval_with_cal.txt' --round 0 --gpu-sets=0 --seeds 1 --ood_method 'csi' --original

Evaluation using pre-trained models

Please download the pre-trained models and calibration parameters by running download_pretrained_models.py or download manually from link. The models and calibration parameters need to be saved under LOG_DIR/NAME_round_NO/, where LOG_DIR is --log_dir (e.g., ./logs/cifar10_5t_csi) and NAME is --name (e.g., cifar10_5t_csi) and NO is the round number (e.g., usually 0).

The provided pre-trained models give the following CIL results

	MNIST	CIFAR10	CIFAR100-10T	CIFAR100-20t	T-ImageNet-5T	T-ImageNet-10T
Sup+CSI	79.62	85.52	64.63	59.28	49.25	45.81
Sup+CSI+c	82.51	87.75	64.67	59.42	49.52	45.96

Different prediction method (Appendix C)

For the CIL prediction method explained in Appendix C, please run

python cil_is_til_times_tp.py

Acknowledgement

The code uses the source code from CSI and SupSup.

k-gyuhak / WPTP