linusericsson/einspace

Official code for our paper "einspace: Searching for Neural Architectures from Fundamental Operations"

einspace: Searching for Neural Architectures from Fundamental Operations

[paper] [project page]

This is the official codebase for einspace, a new expressive search space for neural architecture search.

Diverse architectures can be represented in our expressive space as shown above for ConvNets, transformers and MLP-only networks.

Follow the instructions below to set up the environment, data, and then run an example script.

Environment Setup

We provide a sample setting up script as following:

conda create -n einspace python=3.10 -y
source activate einspace
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html
pip install git+https://github.com/SamsungLabs/zero-cost-nas.git
pip install tqdm scipy einops positional_encodings seaborn sympy h5py librosa
pip install -r requirements.txt
pip install -e .

Data Setup

Please follow the official instructions of UnseenNAS and NASBench360 to setup the dataset, after which you can place the files using the following arrangement.

einspace
|--data
|   |--adinst
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--language
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--multnist
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--cifartile
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--gutenberg
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--isabella
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--geoclassing
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--chesseract
|   |  |__metadata, test_x.npy, test_y.npy ...
|   |--cifar100
|   |   |--cifar100_train.indices
|   |   |--cifar100_valid.indices
|   |   |__cifar-100-python
|   |      |--meta
|   |      |--train
|   |      |__test
|   |--NinaPro
|   |   |__label_test.npy, label_train.npy, label_val.npy, ninapro_test.npy, ninapro_train.npy, ninapro_val.npy
|   |--Spherical
|   |   |__s2_cifar100.gz, spherical_train.indices, spherical_valid.indices
|   |--darcyflow
|   |   |__piececonst_r421_N1024_smooth1.mat, piececonst_r421_N1024_smooth2.mat
|   |--cosmic
|      |--cosmic_test.pt
|      |--cosmic_train.pt
|      |--cosmic_valid.pt
|      |--npy_test  
|      |--npy_train  
|      |--test_dirs.npy  
|      |__train_dirs.npy
|_ ...

Running Experiments

python einspace/main.py --config $config --device $GPU

For example, to execute the RE(RN18) experiment on the Language dataset, you can run

python einspace/main.py --config configs/language/re_language.yaml --device cuda:0

Cite us!

@article{ericsson2024einspace,
    title={einspace: Searching for Neural Architectures from Fundamental Operations}, 
    author={Linus Ericsson and Miguel Espinosa and Chenhongyi Yang and Antreas Antoniou and Amos Storkey and Shay B. Cohen and Steven McDonagh and Elliot J. Crowley},
    year={2024},
    eprint={2405.20838},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}