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https://github.com/ianwhale/nsga-net
NSGA-Net, a Neural Architecture Search Algorithm
https://github.com/ianwhale/nsga-net
auto-ml deep-learning evolutionary-computation neural-architecture-search nsga-ii pytorch
Last synced: 3 months ago
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NSGA-Net, a Neural Architecture Search Algorithm
- Host: GitHub
- URL: https://github.com/ianwhale/nsga-net
- Owner: ianwhale
- Created: 2018-05-23T18:05:26.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-19T14:07:08.000Z (about 5 years ago)
- Last Synced: 2024-06-14T01:27:48.492Z (5 months ago)
- Topics: auto-ml, deep-learning, evolutionary-computation, neural-architecture-search, nsga-ii, pytorch
- Language: Python
- Homepage:
- Size: 2.43 MB
- Stars: 258
- Watchers: 15
- Forks: 78
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - ianwhale/nsga-net
README
# NSGA-Net
Code accompanying the paper. All codes assume running from root directory. Please update the sys path at the beginning of the codes before running.
> [NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm](https://arxiv.org/abs/1810.03522)
>
> Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman and Wolfgang Banzhaf
>
> *arXiv:1810.03522*
## Requirements
```
Python >= 3.6.8, PyTorch >= 1.0.1.post2, torchvision >= 0.2.2, pymoo == 0.3.0
```
## Results on CIFAR-10
## Pretrained models on CIFAR-10
The easiest way to get started is to evaluate our pretrained NSGA-Net models.
#### Macro search space ([NSGA-Net-macro](https://drive.google.com/file/d/173_CXA_YbEjg1_Lnfg6vqweTRDiuDi0J/view?usp=sharing))
``` shell
python validation/test.py --net_type macro --model_path weights.pt
```
- Expected result: *3.73%* test error rate with *3.37M* model parameters, *1240M* Multiply-Adds.
#### Micro search space
``` shell
python validation/test.py --net_type micro --arch NSGANet --init_channels 26 --filter_increment 4 --SE --auxiliary --model_path weights.pt
```
- Expected result: *2.43%* test error rate with *1.97M* model parameters, *417M* Multiply-Adds ([*weights.pt*](https://drive.google.com/open?id=1JvMkT1eo6JegtUvT-5qY4LK3xgq-k-OH)).
``` shell
python validation/test.py --net_type micro --arch NSGANet --init_channels 34 --filter_increment 4 --auxiliary --model_path weights.pt
```
- Expected result: *2.22%* test error rate with *2.20M* model parameters, *550M* Multiply-Adds ([*weights.pt*](https://drive.google.com/open?id=1it_aFoez-U7SkxSuRPYWDVFg8kZwE7E7)).
``` shell
python validation/test.py --net_type micro --arch NSGANet --init_channels 36 --filter_increment 6 --SE --auxiliary --model_path weights.pt
```
- Expected result: *2.02%* test error rate with *4.05M* model parameters, *817M* Multiply-Adds ([*weights.pt*](https://drive.google.com/open?id=1kLXzKxQ7dazjmANTvgSoeMPHWwYKiOtm)).
## Pretrained models on CIFAR-100
``` shell
python validation/test.py --task cifar100 --net_type micro --arch NSGANet --init_channels 36 --filter_increment 6 --SE --auxiliary --model_path weights.pt
```
- Expected result: *14.42%* test error rate with *4.1M* model parameters, *817M* Multiply-Adds ([*weights.pt*](https://drive.google.com/open?id=1CMtSg1l2V5p0HcRxtBsD8syayTtS9QAu)).
## Architecture validation
To validate the results by training from scratch, run
```
# architecture found from macro search space
python validation/train.py --net_type macro --cutout --batch_size 128 --epochs 350
# architecture found from micro search space
python validation/train.py --net_type micro --arch NSGANet --layers 20 --init_channels 34 --filter_increment 4 --cutout --auxiliary --batch_size 96 --droprate 0.2 --SE --epochs 600
```
You may need to adjust the batch_size depending on your GPU memory.
For customized macro search space architectures, change `genome` and `channels` option in `train.py`.
For customized micro search space architectures, specify your architecture in `models/micro_genotypes.py` and use `--arch` flag to pass the name.
## Architecture search
To run architecture search:
``` shell
# macro search space
python search/evolution_search.py --search_space macro --init_channels 32 --n_gens 30
# micro search space
python search/evolution_search.py --search_space micro --init_channels 16 --layers 8 --epochs 20 --n_offspring 20 --n_gens 30
```
Pareto Front | Network
:-------------------------:|:-------------------------:
 | 
Pareto Front | Normal Cell | Reduction Cell
:-------------------------:|:-------------------------:|:-------------------------:
 |  | 
If you would like to run asynchronous and parallelize each architecture's back-propagation training, set `--n_offspring` to `1`. The algorithm will run in *steady-state* mode, in which the population is updated as soon as one new architecture candidate is evaludated. It works reasonably well in single-objective case, a similar strategy is used in [here](https://arxiv.org/abs/1802.01548).
## Visualization
To visualize the architectures:
``` shell
python visualization/macro_visualize.py NSGANet # macro search space architectures
python visualization/micro_visualize.py NSGANet # micro search space architectures
```
For customized architecture, first define the architecture in `models/*_genotypes.py`, then substitute `NSGANet` with the name of your customized architecture.
## Citations
If you find the code useful for your research, please consider citing our works
```
@article{nsganet,
title={NSGA-NET: a multi-objective genetic algorithm for neural architecture search},
author={Lu, Zhichao and Whalen, Ian and Boddeti, Vishnu and Dhebar, Yashesh and Deb, Kalyanmoy and Goodman, Erik and Banzhaf, Wolfgang},
booktitle={GECCO-2019},
year={2018}
}
```
## Acknowledgement
Code heavily inspired and modified from [pymoo](https://github.com/msu-coinlab/pymoo), [DARTS](https://github.com/quark0/darts#requirements) and [pytorch-cifar10](https://github.com/kuangliu/pytorch-cifar).