Single Path One-Shot

Single Path One-Shot by Megvii Research.

Introduction

This repository provides the implementation of Single Path One-Shot Neural Architecture Search with Uniform Sampling.

Our Trained Model / Checkpoint

OneDrive: Link

Supernet

Our trained Supernet weight is in $Link/Supernet/checkpoint-150000.pth.tar, which can be used by Search.

Search

Our search result is in $Link/Search/checkpoint.pth.tar, which can be used by Evaluation.

Evaluation

Out searched models have been trained from scratch, is can be found in $Link/Evaluation/$ARCHITECTURE.

Here is a summary:

Architecture	FLOPs	#Params	Top-1	Top-5
(2, 1, 0, 1, 2, 0, 2, 0, 2, 0, 2, 3, 0, 0, 0, 0, 3, 2, 3, 3)	323M	3.5M	25.6	8.0

Usage

1. Setup Dataset and Flops Table

Download the ImageNet Dataset and move validation images to labeled subfolders. To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh

Download the flops table to accelerate Flops calculation which is required in Uniform Sampling. It can be found in $Link/op_flops_dict.pkl.

We recommend to create a folder data and use it in both Supernet training and Evaluation training.

Here is a example structure of data:

data
|--- train                 ImageNet Training Dataset
|--- val                   ImageNet Validation Dataset
|--- op_flops_dict.pkl     Flops Table

2. Train Supernet

Train supernet with the following command:

cd src/Supernet
python3 train.py --train-dir $YOUR_TRAINDATASET_PATH --val-dir $YOUR_VALDATASET_PATH

3. Search in Supernet with Evolutionary Algorithm

Search in supernet with the following command:

cd src/Search
python3 search.py

It will use ../Supernet/checkpoint-latest.pth.tar as Supernet's weight, please make sure it exists or modify the path manually.

4. Get Searched Architecture

Get searched architecture with the following command:

cd src/Evaluation
python3 eval.py

It will generate folder in data/$YOUR_ARCHITECTURE. You can train the searched architecture from scratch in the folder.

5. Train from Scratch

Finally, train and evaluate the searched architecture with the following command.

Train:

cd src/Evaluation/data/$YOUR_ARCHITECTURE
python3 train.py --train-dir $YOUR_TRAINDATASET_PATH --val-dir $YOUR_VALDATASET_PATH

Evaluate:

cd src/Evaluation/data/$YOUR_ARCHITECTURE
python3 train.py --eval --eval-resume $YOUR_WEIGHT_PATH --train-dir $YOUR_TRAINDATASET_PATH --val-dir $YOUR_VALDATASET_PATH

Citation

If you use these models in your research, please cite:

@article{guo2019single,
        title={Single path one-shot neural architecture search with uniform sampling},
        author={Guo, Zichao and Zhang, Xiangyu and Mu, Haoyuan and Heng, Wen and Liu, Zechun and Wei, Yichen and Sun, Jian},
        journal={arXiv preprint arXiv:1904.00420},
        year={2019}
}

zcoder233/SinglePathOneShot