This is the source code for paper:
NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction

We introduce the experimental process using the NAS-Bench-201 dataset (5% training setting) as an example. The experiments on NAS-Bench-101 are similar to this.

You can directly download datas/nasbench201 and put it in ./data/ or generate by yourself following the steps below:

1. Download NAS-Bench-201-v1_0-e61699.pth and put it in ./data/nasbench201/.

python preprocessing/gen_json_201.py

The generated file cifar10_valid_converged.json will be saved in ./data/nasbench201/.

2. Encode each architecture with our proposed tokenizer.

python data_and_encoding_generate.py --dataset nasbench201 --data_path data/nasbench201/cifar10_valid_converged.json --save_dir data/nasbench201/

The generated file all_nasbench201.pt will be saved in ./data/nasbench201/.

3. (Results in the paper.) If you want to use information flow consistency augmentation, run the following code to generate the augmented data file.

python ac_aug_generate.py --dataset nasbench201 --data_path data/nasbench201/all_nasbench201.pt 

The file of augmented data will be saved in ./data/nasbench201/.

You can directly download pretrained_modes/nasbench201/checkpoints_5%_aug and put it in ./experiment/Accuracy_Predict_NASBench201/ or train from scratch following the steps below:

1. Change the BASE_DIR in script files in folder experiment/Accuracy_Predict_NASBench201/ to the current absolute path.

2. For model training, you can choose to use augmented data or not.

• Without augmented data:

cd experiment/Accuracy_Predict_NASBench201/
bash train_5%.sh

The pretrained models will be saved in ./checkpoints_5%/

• (Results in the paper.) With augmented data:

cd experiment/Accuracy_Predict_NASBench201/
bash train_5%_aug.sh

The pretrained models will be saved in ./checkpoints_5%_aug/

• For models trained without augmented data:

bash test_5%.sh

• (Results in the paper.) For models trained with augmented data:

bash test_5%_aug.sh

If you find our codes or trained models useful in your research, please consider to star our repo and cite our paper:

@article{yi2022nar,
  title={NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction},
  author={Yi, Yun and Zhang, Haokui and Hu, Wenze and Wang, Nannan and Wang, Xiaoyu},
  journal={arXiv preprint arXiv:2211.08024},
  year={2022}
}

1. NAS-Bench-101
2. NAS-Bench-201
3. NNLQP