gaea_release
Geometry-Aware Gradient Algorithms for Neural Architecture Search.
This repository contains the code required to run the experiments for the DARTS search space over CIFAR-10 and the NAS-Bench-201 search space over CIFAR-10, CIFAR-100, and ImageNet16-120. Code to run the experiments on the DARTS search space over ImageNet and the NAS-Bench-1Shot1 search spaces will be made available in forked repos subsequently.
First build the docker image using the provided docker file:
docker build -t [name] -f docker/config.dockerfile .
Then run a container with the image, e.g.:
docker run -it --gpus all --rm [name]
Then run the commands below from within the container. The scripts provided may be helpful.
DARTS Search Space on CIFAR-10
Search using GAEA PC-DARTS by running
python train_search.py
mode=search_pcdarts
nas_algo=eedarts
search_config=method_eedarts_space_pcdarts
run.seed=[int]
run.epochs=50
run.dataset=cifar10
search.single_level=false
search.exclude_zero=false
Evaluate architecture found in search phase by running
python train_aws.py
train.arch=[archname which must be specified in cnn/search_spaces/darts/genotypes.py]
run.seed=[int]
train.drop_path_prob=0.3
NAS-Bench-201 Search Space
Search using GAEA DARTS by running
python train_search.py
mode=search_nasbench201
nas_algo=edarts
search_config=method_edarts_space_nasbench201
run.seed=[int]
run.epochs=25
run.dataset=[one of cifar10, cifar100, or ImageNet16-120]
search.single_level=[true for ERM and false for bilevel]
search.exclude_zero=true
