Single Path One-Shot NAS Pytorch implementation, include Supernet training and searching(sample)
Thanks for the great work of MXNet-Single-Path-One-Shot-NAS, i basiclly follow it.
Model | Official | MXNet repo | this repo |
---|---|---|---|
Subnet Training | √ | √ | √ |
Block Selection | × | √ | √ |
Channel Selection | × | √ | √ |
Supernet Training | × | √ | × |
FLOPs & Parameters Counting Tool | × | √ | √ |
BN Statistics Update | × | √ | √ |
Random Search | × | √ | √ |
Genetic Search | × | √ | √ |
SE | - | √ | √ |
Efficient Last Conv Block | - | √ | × |
Op to Op Profiling Tool | - | √ | × |
Merge BN | - | √ | × |
Int8 Quantization | - | √ | × |
If you use these models in your research, please cite:
@article{guo2019single,
title={Single path one-shot neural architecture search with uniform sampling},
author={Guo, Zichao and Zhang, Xiangyu and Mu, Haoyuan and Heng, Wen and Liu, Zechun and Wei, Yichen and Sun, Jian},
journal={arXiv preprint arXiv:1904.00420},
year={2019}
}
@misc{yan2019sposmxnet,
title={single-path-one-shot-mxnet},
author={Yan, Kang},
howpublished={\url{https://github.com/CanyonWind/Single-Path-One-Shot-NAS-MXNet}},
year={2019}
}