Awesome
FreeREA
Code release for FreeREA: Training-Free Evolution-Based Architecture Search
If you use this code or the attached files for research purposes, please cite
@InProceedings{cavagnero2022freerea,
author = {Cavagnero, Niccol\`o and Robbiano, Luca and Caputo, Barbara and Averta, Giuseppe},
title = {FreeREA: Training-Free Evolution-Based Architecture Search},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
pages = {1493-1502}
}
Software requirements
- Python 3.9 or newer
- PyTorch 1.9 or newer
- Other Python libraries listed in
requirements.txt
Hardware requirements
A CUDA-capable GPU is required to compute the metrics.
However, precomputed metrics for the benchmarks NASBench101 and NATS-Bench are available in the directory cached_metrics
.
Run experiments
Results can be reproduced with:
export NATS_PATH=/data/path/to/NATS-tss-v1_0-3ffb9-simple
./run_search.py --space nats --dataset cifar10
./run_search.py --space nats --dataset cifar100
./run_search.py --space nats --dataset ImageNet16-120
export NASBENCH101_PATH=/data/path/to/NASBench-101/nasbench_full.pkl
./run_search.py --space nasbench101 --dataset cifar10 --max-time 12
License
This code and the attached files are distributed under the MIT license.
Code within the directory nas_spaces/_nasbench101
is derived from this repository and is released under the Apache 2.0 license.
Contributors
- Niccolò Cavagnero niccolo.cavagnero@polito.it
- Luca Robbiano luca.robbiano@polito.it