ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware [Website] [arXiv] [Poster]
@inproceedings{
cai2018proxylessnas,
title={Proxyless{NAS}: Direct Neural Architecture Search on Target Task and Hardware},
author={Han Cai and Ligeng Zhu and Song Han},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://arxiv.org/pdf/1812.00332.pdf},
}
Without any proxy, directly and efficiently search neural network architectures on your target task and hardware!
- Sep-30-2019: Searhcing code is released (under search folder).
- Aug-10-2019: Training code is released (under training folder).
- Dec-21-2018: TensorFlow pretrained models are released.
- Dec-01-2018: PyTorch pretrained models are released.
People used to deploy one model to all platforms, but this is not good. To fully exploit the efficiency, we should specialize architectures for each platform.
Please refer to our paper for more results.
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Use
# pytorch from proxyless_nas import proxyless_cpu, proxyless_gpu, proxyless_mobile, proxyless_mobile_14 net = proxyless_cpu(pretrained=True) # Yes, we provide pre-trained models!
# tensorflow from proxyless_nas_tensorflow import proxyless_cpu, proxyless_gpu, proxyless_mobile, proxyless_mobile_14 tf_net = proxyless_cpu(pretrained=True)
If the above scripts failed to download, you download it manually from Google Drive and put them under
$HOME/.torch/proxyless_nas/
. -
Evaluate
python eval.py --path 'Your path to imagent' --arch proxyless_cpu # pytorch
python eval_tf.py --path 'Your path to imagent' --arch proxyless_cpu # tensorflow
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (ICLR’19)
AMC: AutoML for Model Compression and Acceleration on Mobile Devices (ECCV’18)
HAQ: Hardware-Aware Automated Quantization (CVPR’19, oral)
Defenstive Quantization: When Efficiency Meets Robustness (ICLR'19)