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zhengxiawu f5a36ecece | 1年前 | |
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configs | 1年前 | |
doc | 1年前 | |
search | 1年前 | |
tools | 2 年前 | |
train | 2 年前 | |
xnas | 1年前 | |
.gitignore | 2 年前 | |
LICENSE | 3 年前 | |
README.md | 1年前 | |
environment.yaml | 2 年前 |
XNAS is an effective, modular and flexible neural architecture search (NAS) codebase, which aims to provide a common framework and baselines for the NAS community.
This project is now supported by PengCheng Lab
python search/DARTS.py --cfg configs/search/DARTS.yaml
python search/PDARTS.py --cfg configs/search/PDARTS.yaml
python search/PCDARTS.py --cfg configs/search/PCDARTS.yaml
python search/DrNAS/nb201space.py --cfg configs/search/DrNAS/nb201_cifar10_Dirichlet.yaml
python search/DrNAS/nb201space.py --cfg configs/search/DrNAS/nb201_cifar100_Dirichlet.yaml
python search/DrNAS/DARTSspace.py --cfg configs/search/DrNAS/DARTS_cifar10.yaml
python search/TENAS.py --cfg configs/search/TENAS/nb201_cifar10.yaml
./search/RMINAS/download_weight.sh # prepare weights of teacher models
./python search/RMINAS/RMINAS_nb201.py --cfg configs/search/RMINAS/nb201_cifar10.yaml
./python search/RMINAS/RMINAS_darts.py --cfg configs/search/RMINAS/darts_cifar10.yaml
./python search/DropNAS.py --cfg configs/search/DROPNAS.yaml
git clone https://git.openi.org.cn/PCL_AutoML/XNAS
cd XNAS
# set root path
export PYTHONPATH=$PYTHONPATH:/Path/to/XNAS
# set gpu devices. Multiple GPUs are under test and may cause errors now.
export CUDA_VISIBLE_DEVICES=0
# unit test example
python tools/test_func/sng_function_optimization.py
# train example
python train/DARTS_train.py --cfg configs/search/darts.yaml
# replace config example
python train/DARTS_train.py --cfg configs/search/darts.yaml OUT_DIR /username/project/XNAS/experiment/darts/test1
On cifar10, the network is trained by using the default training set of pt.darts.
We reimplement several widely used NAS methods including:
Method | Trial | params(M) | search(hrs) | train(hrs) | Top1 | Flops(M) | download | Search Top1 | Search Space |
---|---|---|---|---|---|---|---|---|---|
darts | 1 | 4.39 | 21 | 39 | 96.97 | 689.335 | - | 90.32 | cell-based |
darts | 2 | 4.25 | 26.36 | 39 | 97.31 | 680.073 | - | 90.47 | cell-based |
darts | 3 | 4.450 | 27.63 | 39.7 | 97.32 | 708.468 | - | 90.09 | cell-based |
darts | 4 | 4.467 | 21.48 | 48 | 97.39 | 717.454 | - | 90.52 | cell-based |
paper | - | 3.3 | 96 | - | 97.24 | - | - | - | cell-based |
sng | 1 | 3.042 | 2.5 | 33.45 | 96.87 | 506.002 | - | 87.52 | cell-based |
sng | 2 | 2.477 | 3.0 | 26.62 | 96.73 | 397.068 | - | 87.81 | cell-based |
sng | 3 | 2.087 | 3.0 | 21.75 | 96.56 | 339.201 | - | 87.00 | cell-based |
sng | 4 | 3.230 | 2.5 | 27.47 | 97.30 | 509.071 | - | 88.51 | cell-based |
asng | 1 | 2.001 | 2.5 | 18.98 | 96.61 | 330.575 | - | 85.78 | cell-based |
asng | 2 | 2.749 | 2.5 | 25.66 | 96.48 | 450.153 | - | 87.47 | cell-based |
asng | 3 | 2.991 | 2.5 | 27.88 | 97.31 | 476.695 | - | 85.52 | cell-based |
asng | 4 | 2.189 | 2.5 | 23.88 | 96.55 | 350.647 | - | 86.41 | cell-based |
MIGO | 1 | 3.266 | 1.5 | 28.20 | 97.35 | 531.217 | - | 84.75 | cell-based |
MIGO | 2 | 3.274 | 1.5 | 26.33 | 97.41 | 523.973 | - | 84.61 | cell-based |
MIGO | 3 | 2.848 | 1.5 | 25.91 | 97.36 | 451.480 | - | 84.89 | cell-based |
MIGO | 4 | 2.749 | 1.5 | 30.19 | 97.28 | 439.619 | - | 84.44 | cell-based |
pcdarts(official) | 1 | 4.052 | 3.61 | 41.28 | 97.20 | 638.823 | 85.296 | cell-based | |
pcdarts(official) | 2 | 3.247 | 3.6 | 27.96 | 97.23 | 512.444 | 84.552 | cell-based | |
pcdarts(official) | 3 | 4.368 | 3.63 | 38.68 | 97.25 | 688.561 | 84.792 | cell-based | |
pcdarts(official) | 4 | 4.148 | 3.16 | 34.58 | 97.49 | 649.108 | 85.280 | cell-based | |
xnas-pcdarts | 1 | 3.779 | 3.46 | 31.9 | 97.55 | 595.498 | 85.192 | cell-based | |
xnas-pcdarts | 2 | 3.641 | 3.03 | 33.08 | 96.92 | 573.933 | 85.036 | cell-based | |
xnas-pcdarts | 3 | 4.536 | 3.02 | 41.18 | 97.37 | 722.790 | 84.592 | cell-based | |
xnas-pcdarts | 4 | 3.143 | 3.48 | 32.26 | 97.25 | 505.227 | 85.088 | cell-based | |
pdarts(official) | 1 | 4.052 | 3.50 | - | 97.41 | 555.270 | - | cell-based | |
pdarts(official) | 2 | 3.247 | 3.31 | - | 97.25 | 529.419 | - | cell-based | |
pdarts(official) | 3 | 4.368 | 3.39 | - | 97.25 | 545.732 | - | cell-based | |
pdarts(official) | 4 | 4.148 | 4.08 | - | 97.29 | 642.555 | - | cell-based | |
dynamic_ASNG | 1 | 2.901 | 0.0 | 31.77 | 96.86 | 465.193 | - | 78.65 | cell-based |
dynamic_ASNG | 2 | 2.208 | 0.0 | 18.00 | 96.78 | 351.145 | - | 79.2 | cell-based |
dynamic_ASNG | 3 | 2.365 | 0.0 | 19.93 | 96.20 | 387.364 | - | 79.87 | cell-based |
dynamic_ASNG | 4 | 3.466 | 0.0 | 31.35 | 97.11 | 565.058 | - | 79.87 | cell-based |
dynamic_SNG | 1 | 2.245 | 0.0 | 23.98 | 96.28 | 352.693 | - | 78.95 | cell-based |
dynamic_SNG | 2 | 2.927 | 0.0 | 24.13 | 96.87 | 473.156 | - | 78.07 | cell-based |
dynamic_SNG | 3 | 2.724 | 0.0 | 28.07 | 97.45 | 442.826 | - | 77.68 | cell-based |
dynamic_SNG | 4 | 3.323 | 0.0 | 31.85 | 96.65 | 528.784 | - | 79.78 | cell-based |
RMINAS | - | - | 1.92 | 31.9 | 97.36 | - | - | - | cell-based |
@inproceedings{zheng2022rminas,
title={Neural Architecture Search with Representation Mutual Information},
author={Xiawu Zheng, Xiang Fei, Lei Zhang, Chenglin Wu, Fei Chao, Jianzhuang Liu, Wei Zeng, Yonghong Tian, Rongrong Ji},
journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}
@article{zheng2021migo,
title={MIGO-NAS: Towards fast and generalizable neural architecture search},
author={Zheng, Xiawu and Ji, Rongrong and Chen, Yuhang and Wang, Qiang and Zhang, Baochang and Chen, Jie and Ye, Qixiang and Huang, Feiyue and Tian, Yonghong},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2021},
publisher={IEEE}
}
@inproceedings{zheng2020rethinking,
title={Rethinking performance estimation in neural architecture search},
author={Zheng, Xiawu and Ji, Rongrong and Wang, Qiang and Ye, Qixiang and Li, Zhenguo and Tian, Yonghong and Tian, Qi},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11356--11365},
year={2020}
}
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