Breaking the Curse of Space Explosion: Towards Effcient NAS with Curriculum Search
Pytorch implementation for "Breaking the Curse of Space Explosion: Towards Effcient NAS with Curriculum Search".
Requirements
python>=3.7, torch==1.5.0, torchvision==0.6.0, graphviz
Please install all the requirements in requirements.txt.
Datasets
We consider two benchmark classification datsets, including CIFAR-10 and ImageNet.
CIFAR-10 can be automatically downloaded by torchvision.
ImageNet needs to be manually downloaded (preferably to a SSD) following the instructions here.
Training Method
Curriculum search on CIFAR-10
python search.py -o outputs/search
Evaluation Method
- Put the searched architectures in
cnas/model/genotypes.pyas follows.
CNAS = Genotype.from_arch(
normal_arch=[('dil_conv_3x3', 1, 2),
('sep_conv_3x3', 0, 2),
('sep_conv_3x3', 0, 3),
('skip_connect', 1, 3),
('sep_conv_3x3', 1, 4),
('max_pool_3x3', 3, 4),
('sep_conv_3x3', 2, 5),
('sep_conv_3x3', 4, 5)],
normal_concat=[2, 3, 4, 5],
reduced_arch=[('sep_conv_3x3', 0, 2),
('skip_connect', 1, 2),
('dil_conv_5x5', 2, 3),
('skip_connect', 1, 3),
('dil_conv_3x3', 2, 4),
('sep_conv_3x3', 1, 4),
('sep_conv_5x5', 0, 5),
('sep_conv_3x3', 3, 5)],
reduced_concat=[2, 3, 4, 5])
- Evaluate the searched architecture on CIFAR-10 and ImageNet dataset using the following scripts.
Evaluation on CIFAR-10:
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 --master_port=23333 \
eval_arch.py \
--arch CNAS --init_channels 36 --layers 20 \
-o outputs/cifar10
Evaluation on ImageNet:
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch \
--nproc_per_node=4 --master_port=22333 \
eval_arch.py \
--max_epochs 250 --scheduler linear \
--dataset imagenet --data /path/to/imagenet \
--batch_size 64 --no_bias_decay --num_workers 8 \
--arch CNAS --init_channels 48 --layers 14 \
-o outputs/imagenet
Pretrained models
We have released our CNAS ImageNet pretrained model (top-1 accuracy 75.4%, top-5 accuracy 92.6%) on here.
You can use the following codes to load the ptrained models:
from cnas.model.eval import cnas_imagenet
model = cnas_imagenet(pretrained=True)
Citation
If you use any part of our code in your research, please cite our paper:
@InProceedings{guo2020breaking,
title = {Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search},
author = {Guo, Yong and Chen, Yaofo and Zheng, Yin and Zhao, Peilin and Chen, Jian and Huang, Junzhou and Tan, Mingkui},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
year = {2020}
}
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