90 lines
3.4 KiB
Markdown
90 lines
3.4 KiB
Markdown
# Nueral Architecture Search
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This project contains the following neural architecture search algorithms, implemented in PyTorch.
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- Network Pruning via Transformable Architecture Search
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- One-Shot Neural Architecture Search via Self-Evaluated Template Network
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- Searching for A Robust Neural Architecture in Four GPU Hours
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## Requirements and Preparation
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Please install `PyTorch>=1.0.1`, `Python>=3.6`, and `opencv`.
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The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
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Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
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## Network Pruning via Transformable Architecture Search
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Use `bash ./scripts/prepare.sh` to prepare data splits for `CIFAR-10`, `CIFARR-100`, and `ILSVRC2012`.
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If you do not have `ILSVRC2012` data, pleasee comment L12 in `./scripts/prepare.sh`.
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Search the depth configuration of ResNet:
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```
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CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-depth-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
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```
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Search the width configuration of ResNet:
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```
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CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-width-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
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```
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Search for both depth and width configuration of ResNet:
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```
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CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-cifar.sh cifar10 ResNet56 CIFARX 0.47 -1
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```
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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.
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## One-Shot Neural Architecture Search via Self-Evaluated Template Network
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Train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 SETN 96 -1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
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CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN 256 -1
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```
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Searching codes come soon!
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## Searching for A Robust Neural Architecture in Four GPU Hours
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The old version is located in `others/GDAS`.
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Train the searched GDAS-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 GDAS_V1 96 -1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 GDAS_V1 96 -1
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CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1
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```
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Searching codes come soon!
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# Citation
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If you find that this project helps your research, please consider citing some of the following papers:
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```
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@inproceedings{dong2019tas,
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title = {Network Pruning via Transformable Architecture Search},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Neural Information Processing Systems (NeurIPS)},
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year = {2019}
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}
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@inproceedings{dong2019one,
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title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
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year = {2019}
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}
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@inproceedings{dong2019search,
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title={Searching for A Robust Neural Architecture in Four GPU Hours},
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author={Dong, Xuanyi and Yang, Yi},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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pages={1761--1770},
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year={2019}
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}
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```
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