Partial Hypernetworks for Continual Learning

This repository contains the official PyTorch implementation of "Partial Hypernetworks for Continual Learning", accepted to the Conference on Lifelong Learning Agents (CoLLAs - 2023).

Structure

The code structure is inspired by the Avalanche library.

├── hyper_cl                      # PartialHyperCL as a package
    ├── benchmarks
        ├── ...                   # benchmark generators
    ├── metrics
        ├── ...                   # metrics used for the experiments
    ├── models
        ├── ...                   # models used for the experiments
    ├── plugins
        ├── ...                   # Avalanche-based plugins
    ├── strategies
        ├── ...                   # strategies implemented and used in the experiments
    ├── utils
        ├── ...                   # misc. utilities

├── experiments                   # contains scripts and notebooks used for running experiments
                                  # and visualization 
    ├── configs
        ├── ...                   # default hydra configs
    ├── notebooks
        ├── ...                   # notebooks used for visualization
    ├── scripts
        ├── ...                   # scripts for reproducing results in the paper
    train_incremental.py          # main trainer for incremental-learning experiments
    train_multitask.py            # main trainer for joint-training experiments

Running an experiment

This repository is based on the Avalanche library. You can first create a conda enviroment and install required packages using environment.yml:

1. Install conda

2. Clone the repo and create the conda environment:

git clone https://github.com/HamedHemati/CIR.git
conda env create -f environment.yml

Then, start an experiment using the scripts provided under experiments/scripts:

3. Start training (example: partial hypernetwork with different depths):

python -m experiments.train_incremental --multirun \
        experiment=hyper-alg-reg-NM_scifar100 n_classes=10 input_size=32 \
        model=HyperResNet18SH,HyperResNet18SPv1SH,HyperResNet18SPv2SH,HyperResNet18SPv3SH,HyperResNet18SPv4SH \
        model_params.embd_dim=32 model_params.hidden_size_1=50 \
        model_params.hidden_size_2=32 model_params.head_emb_dim=32 \
        optimizer=Adam optim_params.lr=0.001 \
        strategy_params.coef_hnet_replay=0.5 strategy_params.second_order=False \
        strategy_params.train_epochs=100 num_workers=5 device=cpu