This repository contains our PyTorch training code, evaluation code and pretrained models for AlphaNet.

Our implementation is largely based on AttentiveNAS. To reproduce our results, please first download the AttentiveNAS repo, and use our train_alphanet.py for training and test_alphanet.py for testing.

For more details, please see AlphaNet: Improved Training of Supernet with Alpha-Divergence by Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra.

If you find this repo useful in your research, please consider citing our work and AttentiveNAS:

@article{wang2021alphanet,
  title={AlphaNet: Improved Training of Supernet with Alpha-Divergence},
  author={Wang, Dilin and Gong, Chengyue and Li, Meng and Liu, Qiang and Chandra, Vikas},
  journal={arXiv preprint arXiv:2102.07954},
  year={2021}
}

@article{wang2020attentivenas,
  title={AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling},
  author={Wang, Dilin and Li, Meng and Gong, Chengyue and Chandra, Vikas},
  journal={arXiv preprint arXiv:2011.09011},
  year={2020}
}

To reproduce our results:

• Please first download our pretrained AlphaNet models from a Google Drive path and put the pretrained models under your local folder ./alphanet_data

• To evaluate our pre-trained AlphaNet models, from AlphaNet-A0 to A6, on ImageNet with a single GPU, please run:

  python test_alphanet.py --config-file ./configs/eval_alphanet_models.yml --model a[0-6]

  Expected results:

  Name	MFLOPs	Top-1 (%)
  AlphaNet-A0	203	77.87
  AlphaNet-A1	279	78.94
  AlphaNet-A2	317	79.20
  AlphaNet-A3	357	79.41
  AlphaNet-A4	444	80.01
  AlphaNet-A5 (small)	491	80.29
  AlphaNet-A5 (base)	596	80.62
  AlphaNet-A6	709	80.78

• Additionally, here is our pretrained supernet with KL based inplace-KD and here is our pretrained supernet without inplace-KD.

To train our AlphaNet models from scratch, please run:

python train_alphanet.py --config-file configs/train_alphanet_models.yml --machine-rank ${machine_rank} --num-machines ${num_machines} --dist-url ${dist_url}

We adopt SGD training on 64 GPUs. The mini-batch size is 32 per GPU; all training hyper-parameters are specified in train_alphanet_models.yml.

In case you want to search the set of models of your own interest - we provide an example to show how to search the Pareto models for the best FLOPs vs. accuracy tradeoffs in parallel_supernet_evo_search.py; to run this example:

python parallel_supernet_evo_search.py --config-file configs/parallel_supernet_evo_search.yml 

AlphaNet is licensed under CC-BY-NC.

We actively welcome your pull requests! Please see CONTRIBUTING and CODE_OF_CONDUCT for more info.