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PyTorch implementation of "Searching for A Robust Neural Architecture in Four GPU Hours", CVPR 2019
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README.md

README.md

Searching for A Robust Neural Architecture in Four GPU Hours

We propose A Gradient-based neural architecture search approach using Differentiable Architecture Sampler (GDAS). Please find details in our paper.

Figure-1. We utilize a DAG to represent the search space of a neural cell. Different operations (colored arrows) transform one node (square) to its intermediate features (little circles). Meanwhile, each node is the sum of the intermediate features transformed from the previous nodes. As indicated by the solid connections, the neural cell in the proposed GDAS is a sampled sub-graph of this DAG. Specifically, among the intermediate features between every two nodes, GDAS samples one feature in a differentiable way.

Requirements

  • PyTorch 1.0.1
  • Python 3.6
  • opencv
conda install pytorch torchvision cuda100 -c pytorch

Usages

Train the searched CNN on CIFAR

CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_FG cifar10  cut
CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_F1 cifar10  cut
CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_V1 cifar100 cut

Train the searched CNN on ImageNet

CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts-cnn/train-imagenet.sh GDAS_F1 52 14 B128 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts-cnn/train-imagenet.sh GDAS_V1 50 14 B256 -1

Evaluate a trained CNN model

CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path  $TORCH_HOME/cifar.python --checkpoint ${checkpoint-path}
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path  $TORCH_HOME/ILSVRC2012 --checkpoint ${checkpoint-path}
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path  $TORCH_HOME/ILSVRC2012 --checkpoint GDAS-V1-C50-N14-ImageNet.pth

Train the searched RNN

CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh DARTS_V1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh DARTS_V2
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh GDAS
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V2
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh GDAS

Training Logs

You can find some training logs in ./data/logs/.
You can also find some pre-trained models in Google Driver.

Experimental Results

Figure-2. Top-1 and top-5 errors on ImageNet.

Citation

If you find that this project (GDAS) helps your research, please cite the paper:

@inproceedings{dong2019search,
  title={Searching for A Robust Neural Architecture in Four GPU Hours},
  author={Dong, Xuanyi and Yang, Yi},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}
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