Implementation of our paper titled "Rethinking Momentum Knowledge Distillation in Online Continual Learning"
The paper has been accepted at ICML 2024.
.
├── config
│ ├── icml24
│ │ └── all # configs files
│ ├── parser.py # define all command line arguments
├── logs # some logs
├── main.py
├── readme.md # this file
├── requirements.txt
├── results # accuracy and params used is saved here
├── src
│ ├── buffers # A lot of buffers. Only reservoir is used.
│ ├── datasets # dataset definitions
│ ├── learners
│ │ ├── baselines # original methods
│ │ │ ├── agem.py
│ │ │ ├── derpp.py
│ │ │ ├── dvc.py
│ │ │ ├── er_ace.py
│ │ │ ├── er.py
│ │ │ ├── gsa.py
│ │ │ ├── ocm.py
│ │ │ ├── pcr.py
│ │ ├── base.py
│ │ ├── ce.py
│ │ ├── ema # variations with MKD - EMA
│ │ │ ├── base_ema.py
│ │ │ ├── derpp_ema.py
│ │ │ ├── dvc_ema.py
│ │ │ ├── er_ace_ema.py
│ │ │ ├── er_ema.py
│ │ │ ├── gsa_ema.py
│ │ │ ├── ocm_ema.py
│ │ │ ├── pcr_ema.py
│ │ │ └── tens.py
│ │ ├── er_kdu.py
│ │ └── sdp
│ │ ├── derpp_sdp.py
│ │ ├── dvc_sdp.py
│ │ ├── er_ace_sdp.py
│ │ ├── er_sdp.py
│ │ ├── gsa_sdp.py
│ │ ├── ocm_ema.py
│ ├── models # networks definitions
│ │ ├── cnn.py
│ │ ├── mlp.py
│ │ ├── pcr_resnet.py
│ │ ├── resnet.py
│ │ └── resnet_sdp.py
│ └── utils
│ ├── alias_multinomial.py
│ ├── augment.py
│ ├── data.py
│ ├── early_stopping.py # deprecated
│ ├── losses.py
│ ├── metrics.py
│ ├── name_match.py # here you can see all methods and buffer
│ ├── tensorboard.py # deprecated
│ └── utils.py
├── sweeps # sweeps logs when using sweep (with wandb)
└── wandb # wanbd logs when using wandbIt is recommended to use a virtualenv or docker to run this code. Corresponding dependencies can be found in requirements.txt. In you use pip you can run pip install -r requirements.txt to install required packages.
Command line usage of the current repository is described here.
usage: main.py [-h] [--config CONFIG] [--train] [--test]
[-b BATCH_SIZE] [--learning-rate LEARNING_RATE]
[--momentum M] [--weight-decay W] [--optim {Adam,SGD}] [--save-ckpt]
[--seed SEED] [--tag TAG]
[--results-root RESULTS_ROOT] [--tensorboard] [--verbose]
[--ckpt-root CKPT_ROOT] [--resume] [--model-state MODEL_STATE]
[--buffer-state BUFFER_STATE] [--head HEAD] [--proj-dim PROJ_DIM]
[--nb-channels NB_CHANNELS] [--eval-proj] [--pretrained]
[--supervised] [--dim-int DIM_INT] [-nf NF]
[--data-root-dir DATA_ROOT_DIR] [--min-crop MIN_CROP]
[--dataset {cifar10,cifar100,tiny,imagenet100}]
[--training-type {uni,inc,blurry}] [--n-classes N_CLASSES]
[--img-size IMG_SIZE] [--num-workers NUM_WORKERS] [--n-tasks N_TASKS]
[--labels-order LABELS_ORDER [LABELS_ORDER ...]]
[--blurry-scale BLURRY_SCALE] [--temperature T] [--mem-size MEM_SIZE]
[--mem-batch-size MEM_BATCH_SIZE] [--buffer BUFFER]
[--drop-method {random}] [--mem-iters MEM_ITERS] [--learner LEARNER]
[--eval-mem] [--eval-random]
[--kornia] [--no-kornia] [--tf-type {full,partial}]
[--derpp-alpha DERPP_ALPHA]
[--derpp-beta DERPP_BETA] [--no-wandb] [--wandb-watch] [--sweep]
Pytorch implementation of various continual learners.
options:
-h, --help show this help message and exit
--config CONFIG Path to the configuration file for the training to launch.
-b BATCH_SIZE, --batch-size BATCH_SIZE
mini-batch size (default: 10)
--learning-rate LEARNING_RATE, -lr LEARNING_RATE
Initial learning rate
--momentum M momentum
--weight-decay W, --wd W
weight decay (default: 0)
--optim {Adam,SGD}
--save-ckpt whether to save chekpoints or not
--seed SEED Random seed to use.
--tag TAG, -t TAG Base name for graphs and checkpoints
--results-root RESULTS_ROOT
Where you want to save the results ?
--ckpt-root CKPT_ROOT
Directory where to save the model.
--resume, -r Resume old training. Setup model state and buffer state.
--model-state MODEL_STATE
--buffer-state BUFFER_STATE
--head HEAD
--proj-dim PROJ_DIM
--nb-channels NB_CHANNELS
Number of channels for the input image.
--eval-proj Use projection for inference. (default is representation.)
--pretrained Use a pretrained model if available.
--supervised Pseudo labels or true labels ?
--dim-int DIM_INT
-nf NF Number of feature for Resnet18. Set nf=20 for reduced
resnet18, nf=64 for full.
--data-root-dir DATA_ROOT_DIR
Root dir containing the dataset to train on.
--min-crop MIN_CROP Minimum size for cropping in data augmentation. range (0-1)
--dataset {mnist,fmnist,cifar10,cifar100,tiny,sub,yt}, -d {mnist,fmnist,cifar10,cifar100,tiny,sub,yt}
Dataset to train on
--training-type {uni,inc,blurry}
How to feed the data to the network (incremental context or
not)
--n-classes N_CLASSES
Number of classes in database.
--img-size IMG_SIZE Size of the square input image
--num-workers NUM_WORKERS, -w NUM_WORKERS
Number of workers to use for dataloader.
--n-tasks N_TASKS How many tasks do you want ?
--labels-order LABELS_ORDER [LABELS_ORDER ...]
In which order to you want to see the labels ? Random if not
specified.
--blurry-scale BLURRY_SCALE
--temperature T temperature parameter for softmax
--mem-size MEM_SIZE Memory size for continual learning
--mem-batch-size MEM_BATCH_SIZE, -mbs MEM_BATCH_SIZE
How many images do you want to retrieve from the memory/ltm
--buffer BUFFER What buffer do you want ? See available buffers in
utils/name_match.py
--mem-iters MEM_ITERS
Number of iterations on memory
--learner LEARNER What learner do you want ? See list of available learners in
utils/name_match.py
--eval-mem
--eval-random
--n-runs N_RUNS Number of runs, with different seeds each time.
--kornia
--no-kornia
--n-augs N_AUGS
--tf-type {full,partial}
Data augmentation sequence to use.
--derpp-alpha DERPP_ALPHA
Values of alpha un der++ loss
--derpp-beta DERPP_BETA
Values of beta un der++ loss
--no-wandb
--wandb-watch Watch the models gradient and parameters into wandb (can be
slow).
--sweep Run the code with sweep for HP search.Training can be done by specifying parameters in command line, for example:
python main.py --results-root results/cifar10/ --data-root /data/dataset/torchvision --learner ER_EMA --dataset cifar10 --batch-size 10 --optim Adam --learning-rate 0.0005When using a configuration file, parameters specified in the .yaml cannot be overriten by command line arguments. However, other parameters like --data-root can be adapted to the users' need.
python main.py --data-root /data/dataset/torchvision --config config/icml24/all/ER,cifar10,m1000mbs64sbs10,blurry500.yamlOutput of the command above should contain performances like this.
root - INFO - --------------------FORGETTING--------------------
root - INFO - ncm 0.0000 nan nan nan nan 0.0000
root - INFO - ncm 0.2885 0.0000 nan nan nan 0.2885
root - INFO - ncm 0.2935 0.2225 0.0000 nan nan 0.2580
root - INFO - ncm 0.4615 0.3190 0.0370 0.0000 nan 0.2725
root - INFO - ncm 0.5815 0.2155 0.1795 0.0250 0.0000 0.2504
root - INFO - --------------------ACCURACY--------------------
root - INFO - ncm 0.7750 nan nan nan nan 0.7750
root - INFO - ncm 0.4865 0.5260 nan nan nan 0.5062
root - INFO - ncm 0.4815 0.3035 0.5150 nan nan 0.4333
root - INFO - ncm 0.3135 0.2070 0.4780 0.2875 nan 0.3215
root - INFO - ncm 0.1935 0.3105 0.3355 0.2625 0.3045 0.2813@InProceedings{pmlr-v235-michel24a,
title = {Rethinking Momentum Knowledge Distillation in Online Continual Learning},
author = {Michel, Nicolas and Wang, Maorong and Xiao, Ling and Yamasaki, Toshihiko},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {35607--35622},
year = {2024},
editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
volume = {235},
series = {Proceedings of Machine Learning Research},
month = {21--27 Jul},
publisher = {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/michel24a/michel24a.pdf},
url = {https://proceedings.mlr.press/v235/michel24a.html},
abstract = {Online Continual Learning (OCL) addresses the problem of training neural networks on a continuous data stream where multiple classification tasks emerge in sequence. In contrast to offline Continual Learning, data can be seen only once in OCL, which is a very severe constraint. In this context, replay-based strategies have achieved impressive results and most state-of-the-art approaches heavily depend on them. While Knowledge Distillation (KD) has been extensively used in offline Continual Learning, it remains under-exploited in OCL, despite its high potential. In this paper, we analyze the challenges in applying KD to OCL and give empirical justifications. We introduce a direct yet effective methodology for applying Momentum Knowledge Distillation (MKD) to many flagship OCL methods and demonstrate its capabilities to enhance existing approaches. In addition to improving existing state-of-the-art accuracy by more than $10%$ points on ImageNet100, we shed light on MKD internal mechanics and impacts during training in OCL. We argue that similar to replay, MKD should be considered a central component of OCL. The code is available at https://github.com/Nicolas1203/mkd_ocl.}
}