This is the offical code of Accelerating Neural Architecture Search via Proxy Data accepted in IJCAI 2021.
Especially, we provide an implementation of DARTS with proxy data.
Python >= 3.6.10, PyTorch == 1.4.0, torchvision == 0.5.0
While CIFAR-10, CIFAR-100, and SVHN can be automatically downloaded by torchvision, ImageNet needs to be manually downloaded (preferably to a SSD) following the instructions here.
To execute DARTS with proxy data of CIFAR-10 (default sampling portion: 0.1), run
python train_search_proxy_data.py --gpu 0 --histogram --histogram_type 1
Please see script.sh
to enjoy various examples for running the search code.
Several neural networks searched in this study are included in genotypes.py
.
To train a searched neural network (e.g., named your_arch) on CIFAR-10, run
python train.py --gpu 0 --arch *your_arch*
To train a searched neural network on ImageNet, run
CUDA_VISIBLE_DEVICES="0,1" python train_imagenet.py --arch *your_arch* --datadir IMAGENET_PATH --parallel --batch_size 384