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FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search arXiv

Neural Architecture Search (NAS) has emerged as a key tool in identifying optimal configurations of deep neural networks tailored to specific tasks. However, training and assessing numerous architectures introduces considerable computational overhead. We propose a GIN predictor and new training procedure for performance evaluation in NAS.

Train and Evaluation

run experiments/predictor/predictor_compare.py for prediction compare.

The results are stored in experiments/predictor/result).

Process the result using code in experiments/predictor/result_parser.py.

Source File Structure

  • experiments: Experiments conducted for the paper.
  • nas_lib/model_predictor:
    • agent: Neural predictors.
    • search_space: Search spaces, including database interactions and data preprocessing.
    • trainer: Training procedures for models.

Environment Setup

  • python=3.8
  • scipy=1.4.1
  • torch=1.12.1+cu116
  • torch-geometric

Installing EvoXBench

To utilize the EvoXBench database, configure it after installation:

from evoxbench.database.init import config

config("Path to database", "Path to data")
# For instance:
# With this structure:
# /home/Downloads/
# └─ database/
# |  |  __init__.py
# |  |  db.sqlite3
# |  |  ...
# |
# └─ data/
#    └─ darts/
#    └─ mnv3/
#    └─ ...
# Then, execute:
# config("/home/Downloads/database", "/home/Downloads/data")

Installation Commands

For Windows (CPU version)

# pytorch 1.12.1
pip install torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1
# torch-geometric
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcpu/torch_cluster-1.6.0-cp38-cp38-win_amd64.whl
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcpu/torch_scatter-2.0.9-cp38-cp38-win_amd64.whl
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcpu/torch_sparse-0.6.14-cp38-cp38-win_amd64.whl
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcpu/torch_spline_conv-1.2.1-cp38-cp38-win_amd64.whl
pip install torch-geometric

For Windows (CUDA 11.3 version)

# pytorch 1.12.1
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
# torch-geometric
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcu113/torch_cluster-1.6.0-cp38-cp38-win_amd64.whl
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcu113/torch_scatter-2.0.9-cp38-cp38-win_amd64.whl
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcu113/torch_sparse-0.6.14-cp38-cp38-win_amd64.whl
pip install https://data.pyg.org/whl/torch-1.12.0%2Bcu113/torch_spline_conv-1.2.1-cp38-cp38-win_amd64.whl
pip install torch-geometric

Citing FR-NAS

If you use FR-NAS in your research and want to cite it in your work, please use:

@misc{zhang2024frnas,
      title={FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search}, 
      author={Haoming Zhang and Ran Cheng},
      booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
      year={2024}
}

Acknowledge

This code is implemented based on the framework provided by NPENASv1