AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling

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

For details see AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling by Dilin Wang, Meng Li, Chengyue Gong and Vikas Chandra.

If you find this project useful in your research, please consider cite:

@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}
}

Pretrained models and data

Download our pretrained AttentiveNAS models and a (sub-network, FLOPs) lookup table from Google Drive and put them under folder ./attentive_nas_data

Evaluation

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

python test_attentive_nas.py --config-file ./configs/eval_attentive_nas_models.yml --model a[0-6]

Expected results:

Training

To train our AttentiveNAS models from scratch, run

python train_supernet.py --config-file configs/train_attentive_nas_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_attentive_nas_models.yml.

License

The majority of AttentiveNAS is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Once For All is licensed under the Apache 2.0 license.

Contributing

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