ODIN

Code for paper:

"Adaptive Neural Networks for Online Domain Incremental Continual Learning" Online Domain Incremental Networks (ODIN) Discovery Science 2022 Nuwan Gunasekara, Heitor Gomes, Albert Bifet, and Bernhard Pfahringer AI Institute, University of Waikato, Hamilton, New Zealand.

How to get the code

git clone --recurse-submodules --branch ODIN git@github.com:nuwangunasekara/avalanche.git ~/ODIN/avalanche

verify you are using the correct git tag (ODIN)

% cd ~/ODIN/avalanche
% git reflog                          
a6b017ef (HEAD, tag: ODIN, origin/master, origin/HEAD) HEAD@{0}: clone: from github.com:nuwangunasekara/avalanche.git

Take the following steps to create an environment and have the code run after cloning the code.

1. Download spec-file.txt

2. conda create --prefix ~/ODIN/CL_conda --file spec-file.txt

   • ~/ODIN/CL_conda is the direction where one wants to create an environment
   • spec-file.txt is the list of the used library and should be replaced in the directory your terminal shows as active.

3. conda env list: Check whether the environment has been created.

4. conda activate ~/ODIN/CL_conda

   • ~/ODIN/CL_conda shows the created environment's directory.

5. Clone scikit-multiflow on your system: git clone https://github.com/scikit-multiflow/scikit-multiflow.git ~/ODIN/scikit-multiflow

   • It is important to clone scikit-multiflow on your system and install it manually, do not install it from the website directly to avoid upgrading Numpy and other libraries.

6. pip install -e ~/ODIN/scikit-multiflow

   • ~/ODIN/scikit-multiflow is the directory where you have cloned skmultiflow

7. cd ~/ODIN/avalanche: Change the directory to the cloned place of the avalanche

8. pip install -e .: Install the avalanche library on the created environment

9. pip install gdown

10. pip install scikit-image==0.19.0

    • 0.19.0: : version should be stated to avoid upgrading Numpy

11. Run an experiment: bash exp_scripts/run_exp.sh exp_scripts/train_pool.py ~/ODIN/results ~/ODIN/CL_conda

    • ~/ODIN/results is the path where the results will be saved
    • ~/ODIN/CL_conda is the created environment directory

Consider the following points:

1. If one's system does not support quantize approach, in the line 618 from Multi-MLP, put the quantize variable as False.

2. If, in the middle of the process, you decide to start from scratch, make sure to remove the created file before:

   • Make sure you delete existing conda env (if there is any) and results directory

     rm -rf  ~/ODIN/results ~/ODIN/CL_conda
     

Run experiments:

You could use use exp_scripts/run_exp.sh script to run multiple experiments. Please edit it for your needs before you run (running step 11).

1. One could tail the logfile output to continually monitor the status of the experiment.
2. One could also use exp_scripts/train_pool.py python script to run a single experiment. Please have a look at the command line arguments before you run.

Notes

Please let us know if you come across any issue with the build or running experiments

Special thanks to pouya1996 for improving this installation instructions.