conda create -n inrprune python=3.7
conda activate inrprune

conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c nvidia

pip install torchmeta
pip install imageio einops tensorboardX

# Train dense model first
python main.py --exp meta_baseline --epoch 150000 --data <DATASET>

# Iterative pruning (magnitude pruning)
python main.py --exp metaprune --epoch 30000 --pruner MP --amount 0.2 --data <DATASET>

# Train dense model first
python main.py --exp meta_baseline --epoch 150000 --data <DATASET>

# Iterative pruning (random pruning)
python main.py --exp metaprune --epoch 30000 --pruner RP --amount 0.2 --data <DATASET>

# Train dense model with a given width

# Shell script style
widthlist="230 206 184 164 148 132 118 106 94 84 76 68 60 54 48 44 38 34 32 28"
for width in $widthlist
do
    python main.py --exp meta_baseline --epoch 150000 --data <DATASET> --width $width --id width_$width
done

python eval.py --exp prune --pruner MP --data <DATASET> --opt_type <OPT_TYPE>

# Random pruning
python eval.py --exp prune --pruner RP --data <DATASET> --opt_type <OPT_TYPE>

# Dense-Narrow
python eval.py --exp dense_narrow --data <DATASET> --opt_type <OPT_TYPE>

# MAML + One-Shot
python eval.py --exp one_shot --data <DATASET> --opt_type default

# MAML + IMP
python eval.py --exp imp --data <DATASET> --opt_type default

# Scratch
python eval.py --exp scratch --data <DATASET> --opt_type <OPT_TYPE>

@inproceedings{lee2021meta,
  title={Meta-learning Sparse Implicit Neural Representations},
  author={Jaeho Lee and Jihoon Tack and Namhoon Lee and Jinwoo Shin},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021}
}