https://github.com/pprp/ofa-cifar
:star: Make Once for All support CIFAR10 dataset.
https://github.com/pprp/ofa-cifar
cifar10 cifar10-classification nas neural-architecture-search
Last synced: 7 months ago
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:star: Make Once for All support CIFAR10 dataset.
- Host: GitHub
- URL: https://github.com/pprp/ofa-cifar
- Owner: pprp
- License: mit
- Created: 2021-09-06T00:58:49.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-09-11T00:35:29.000Z (about 4 years ago)
- Last Synced: 2025-01-31T13:43:54.717Z (9 months ago)
- Topics: cifar10, cifar10-classification, nas, neural-architecture-search
- Language: Python
- Homepage:
- Size: 2.87 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Once for All - CIFAR10
[TOC]
## Introduction
[Once for all](https://github.com/mit-han-lab/once-for-all) is an one-stage one-shot Neural Architecture Search Algorithm, which mainly support ImageNet Datasets.
In this repository, most codes are from https://github.com/mit-han-lab/once-for-all.
We mainly focus on training OFA(Once for all) on CIFAR10 dataset.
What we do:
- Support CIFAR10 dataloader
- Modify training codes
- Support Single GPU Training
- Rewrite code about Max Teachernet Training
- Release TeacherNet weight(Coming soon..)
## How to train **OFA Networks**
```bash
mpirun -np 32 -H :8,:8,:8,:8 \
-bind-to none -map-by slot \
-x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
python train_ofa_net.py
```
or
```bash
horovodrun -np 32 -H :8,:8,:8,:8 \
python train_ofa_net.py
```
## Requirement
* Python 3.6+
* Pytorch 1.4.0+
* ImageNet Dataset
* Horovod
## How to use / evaluate **OFA Networks**
### Use
```python
""" OFA Networks.
Example: ofa_network = ofa_net('ofa_mbv3_d234_e346_k357_w1.0', pretrained=True)
"""
from ofa.model_zoo import ofa_net
ofa_network = ofa_net(net_id, pretrained=True)
# Randomly sample sub-networks from OFA network
ofa_network.sample_active_subnet()
random_subnet = ofa_network.get_active_subnet(preserve_weight=True)
# Manually set the sub-network
ofa_network.set_active_subnet(ks=7, e=6, d=4)
manual_subnet = ofa_network.get_active_subnet(preserve_weight=True)
```
If the above scripts failed to download, you download it manually from [Google Drive](https://drive.google.com/drive/folders/10leLmIiMtaRu4J46KwrBaMydvQt0qFuI?usp=sharing) and put them under $HOME/.torch/ofa_nets/.
### Evaluate
```
python eval_ofa_net.py --path 'Your path to imagenet' --net ofa_mbv3_d234_e346_k357_w1.0
```
## How to use / evaluate **OFA Specialized Networks**
### Use
```python
""" OFA Specialized Networks.
Example: net, image_size = ofa_specialized('flops@595M_top1@80.0_finetune@75', pretrained=True)
"""
from ofa.model_zoo import ofa_specialized
net, image_size = ofa_specialized(net_id, pretrained=True)
```
If the above scripts failed to download, you download it manually from [Google Drive](https://drive.google.com/drive/folders/1ez-t_DAHDet2fqe9TZUTJmvrU-AwofAt?usp=sharing) and put them under $HOME/.torch/ofa_specialized/.
### Evaluate
```
python eval_specialized_net.py --path 'Your path to imagent' --net flops@595M_top1@80.0_finetune@75
```
```BibTex
@inproceedings{
cai2020once,
title={Once for All: Train One Network and Specialize it for Efficient Deployment},
author={Han Cai and Chuang Gan and Tianzhe Wang and Zhekai Zhang and Song Han},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://arxiv.org/pdf/1908.09791.pdf}
}
```