zhuyiche / ProxylessNAS

[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. https://arxiv.org/abs/1812.00332

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

@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 search neural network architectures on your target task and hardware!

Website, arXiv

Requirements

• PyTorch 0.3.1 or Tensorflow 1.5
• Python 3.6+

Updates

• Dec-21-2018: TensorFlow pretrained models are released.
• Dec-01-2018: PyTorch pretrained models are released.

Performance

Mobile settings	GPU settings
Model Top-1 Top-5 Latency FLOPs MobilenetV1 70.6 89.5 113ms 575M MobilenetV2 72.0 91.0 75ms 300M MNasNet(our impl) 74.0 91.8 79ms 317M ProxylessNAS (mobile) 74.6 92.2 78ms 320M ProxylessNAS (mobile_14) 76.7 93.3 147ms 581M	Model Top-1 Top-5 Latency MobilenetV2 72.0 91.0 6.1ms ShufflenetV2(1.5) 72.6 - 7.3ms ResNet-34 73.3 91.4 8.0ms MNasNet(our impl) 74.0 91.8 6.1ms ProxylessNAS (GPU) 75.1 92.5 5.1ms
2.6% better than MobilenetV2 with same speed.	3.1% better than MobilenetV2 with 20% faster.

ProxylessNAS consistently outperforms MobileNetV2 under various latency settings.

Specialization

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.

How to use / evaluate

• 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)

• 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