NAS-Bench-Macro

This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021.

NAS-Bench-Macro is a NAS benchmark on macro search space. The NAS-Bench-Macro consists of 6561 networks and their test accuracies, parameters, and FLOPs on CIFAR-10 dataset.

Each architecture in NAS-Bench-Macro is trained from scratch isolatedly.

Benchmark

All the evaluated architectures are stored in file nas-bench-macro_cifar10.json with the following format:

{
    arch1: {
        test_acc: [float, float, float], // the test accuracies of three independent training
        mean_acc: float, // mean accuracy
        std: float, // the standard deviation of test accuracies
        params: int, // parameters
        flops: int, // FLOPs 
    },
    arch2: ......
}

Search Space

The search space of NAS-Bench-Macro is conducted with 8 searching layers; each layer contains 3 candidate blocks, marked as Identity, MB3_K3, and MB6_K5.

• Identity: identity connection (encoded as '0')
• MB3_K3: MobileNetV2 block with kernel size 3 and expansion ratio 3
• MB6_K5: MobileNetV2 block with kernel size 5 and expansion ratio 6

Network structure

Statistics

Visualization of the best architecture

Histograms

Reproduce the Results

Requirements

torch>=1.0.1
torchvision

Training scripts

cd train
python train_benchmark.py

The test result of each architecture will be stored into train/bench-cifar10/<arch>.txt

After all the architectures are trained, you can collect the results into a final benchmark file:

python collect_benchmark.py

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Citation

If you find that NAS-Bench-Macro helps your research, please consider citing it:

@inproceedings{su2021prioritized,
  title={Prioritized Architecture Sampling with Monto-Carlo Tree Search},
  author={Su, Xiu and Huang, Tao and Li, Yanxi and You, Shan and Wang, Fei and Qian, Chen and Zhang, Changshui and Xu, Chang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10968--10977},
  year={2021}
}