This project contains the following neural architecture search (NAS) algorithms, implemented in PyTorch. More NAS resources can be found in Awesome-NAS.
- NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020
- Network Pruning via Transformable Architecture Search, NeurIPS 2019
- One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
- Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019
- 10 NAS algorithms for the neural topology in
exps/algos(see NAS-Bench-201.md for more details) - Several typical classification models, e.g., ResNet and DenseNet (see BASELINE.md)
Please install PyTorch>=1.2.0, Python>=3.6, and opencv.
CIFAR and ImageNet should be downloaded and extracted into $TORCH_HOME.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from Google Driver (or train by yourself) and save into .latent-data.
- Compute the number of parameters and FLOPs of a model:
from utils import get_model_infos
flop, param = get_model_infos(net, (1,3,32,32))
- Different NAS-searched architectures are defined here.
We build a new benchmark for neural architecture search, please see more details in NAS-Bench-201.md.
The benchmark data file (v1.0) is NAS-Bench-201-v1_0-e61699.pth, which can be downloaded from Google Drive.
Now you can simply use our API by pip install nas-bench-201.
In this paper, we proposed a differentiable searching strategy for transformable architectures, i.e., searching for the depth and width of a deep neural network. You could see the highlight of our Transformable Architecture Search (TAS) at our project page.
Use bash ./scripts/prepare.sh to prepare data splits for CIFAR-10, CIFARR-100, and ILSVRC2012.
If you do not have ILSVRC2012 data, pleasee comment L12 in ./scripts/prepare.sh.
args: cifar10 indicates the dataset name, ResNet56 indicates the basemodel name, CIFARX indicates the searching hyper-parameters, 0.47/0.57 indicates the expected FLOP ratio, -1 indicates the random seed.
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-depth-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-width-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-shape-cifar.sh cifar10 ResNet56 CIFARX 0.47 -1
If you want to directly train a model with searched configuration of TAS, try these:
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/tas-infer-train.sh cifar10 C010-ResNet32 -1
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/tas-infer-train.sh cifar100 C100-ResNet32 -1
The searched shapes for ResNet-20/32/56/110/164 in Table 3 in the original paper are listed in configs/NeurIPS-2019.
Highlight: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
Please use the following scripts to train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 SETN 96 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN 256 -1
The searching codes of SETN on a small search space:
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 -1
We proposed a Gradient-based searching algorithm using Differentiable Architecture Sampling (GDAS). GDAS is baseed on DARTS and improves it with Gumbel-softmax sampling. Experiments on CIFAR-10, CIFAR-100, ImageNet, PTB, and WT2 are reported.
Please use the following scripts to train the searched GDAS-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 GDAS_V1 96 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 GDAS_V1 96 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1
Please use the following scripts to use GDAS to search as in the original paper:
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/GDAS-search-NASNet-space.sh cifar10 1 -1
The GDAS searching codes on a small search space:
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 -1
The baseline searching codes are DARTS:
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 -1
To train the searched architecture found by the above scripts, please use the following codes:
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-201/train-a-net.sh '|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|' 16 5
|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2| represents the structure of a searched architecture. My codes will automatically print it during the searching procedure.
If you find that this project helps your research, please consider citing some of the following papers:
@inproceedings{dong2020nasbench201,
title = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},
year = {2020}
}
@inproceedings{dong2019tas,
title = {Network Pruning via Transformable Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Neural Information Processing Systems (NeurIPS)},
year = {2019}
}
@inproceedings{dong2019one,
title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
pages = {3681--3690},
year = {2019}
}
@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)},
pages = {1761--1770},
year = {2019}
}