XinbangZhang / DSO-NAS

Codes for papers "You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization"

DSO-NAS

Codes for papers "You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization"

You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization
Xinbang zhang, Zehao Huang, Naiyan Wang.

Requirements

python == 2.7 
mxnet == 1.2.1

Cifar Experiments:

Please follow the instructions in https://mxnet.incubator.apache.org/ to make .rec file with the .lst file in /data/cifar, split training data into two part (train1.rec & train2.rec) and put them in /data/cifar_search.

Step1: go to config/cfgs, set dataset='cifar10_search', train_baseline=True, retrain_model=False, model_search=False and run

python train.py

Step2: go to config/cfgs, set dataset='cifar10_search', train_baseline=False,retrain_model=False,model_search=True. To apply share search space, set share_search_space to True. To apply Adaptive Flops or Adaptive MAC technique, please set ad_flops or ad_mac to True. Run

python train_search.py

Loading the pretrain model obtained by step1 is recommonded.

Step3: go to config/cfgs, set dataset='cifar10', train_baseline=False,retrain_model=True,model_search=False, and load the model obtained by step 2 and run

python train.py

name	Test error	Parameter
DSO-NAS-share+cutout	2.74	3.0M
DSO-NAS-full+cutout	2.83	3.0M

ImageNet Experiments:

Please follow the instructions in https://mxnet.incubator.apache.org/ to make .rec file with the .lst file in /data/imagenet and /data/imagenet_search

Step1: go to config/cfgs, set dataset='imagenet_search', train_baseline=True,retrain_model=False,model_search=False and run

python train.py

Step2: go to config/cfgs, set dataset='imagenet_search', train_baseline=False,retrain_model=False,model_search=True. To apply share search space, set share_search_space to True. To apply Adaptive Flops or Adaptive Latency technique, please set ad_flops ad_latency to True. Run

python train_search.py

Loading the pretrain model obtained by step1 is recommonded.

Step3: go to config/cfgs, set dataset='imagenet_search', train_baseline=False,retrain_model=True,model_search=False, and load the model obtained by step 2 and run

python train.py

name	Test error (Top 1 / Top 5)	Parameter	Flops
DSO-NAS-share	25.4 / 8.3	3.0M	567M
DSO-NAS-full	25.7 / 8.1	3.0M	608M

Bibtex

@article{zhang2018you,
  title={You only search once: Single shot neural architecture search via direct sparse optimization},
  author={Zhang, Xinbang and Huang, Zehao and Wang, Naiyan},
  journal={arXiv preprint arXiv:1811.01567},
  year={2018}
}