autodl-projects/docs/CVPR-2019-GDAS.md

# [Searching for A Robust Neural Architecture in Four GPU Hours](https://arxiv.org/abs/1910.04465)

<img align="right" src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300">

Searching for A Robust Neural Architecture in Four GPU Hours is accepted at CVPR 2019.
In this paper, we proposed a Gradient-based searching algorithm using Differentiable Architecture Sampling (GDAS).
GDAS is baseed on DARTS and improves it with Gumbel-softmax sampling.
Concurrently at the submission period, several NAS papers (SNAS and FBNet) also utilized Gumbel-softmax sampling. We are different at how to forward and backward, see more details in our paper and codes.
Experiments on CIFAR-10, CIFAR-100, ImageNet, PTB, and WT2 are reported.


## Requirements and Preparation

Please install `Python>=3.6` and `PyTorch>=1.2.0`.

CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.

### Usefull tools
1. Compute the number of parameters and FLOPs of a model:
```
from utils import get_model_infos
flop, param  = get_model_infos(net, (1,3,32,32))
```

2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).


## Usage

### Reproducing the results of our searched architecture in GDAS
Please use the following scripts to train the searched GDAS-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10  GDAS_V1 96 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 GDAS_V1 96 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1
```
If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).

### Searching on the NASNet search space
Please use the following scripts to use GDAS to search as in the original paper:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/GDAS-search-NASNet-space.sh cifar10 1 -1
```
If you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).

### Searching on a small search space (NAS-Bench-201)
The GDAS searching codes on a small search space:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 1 -1
```

The baseline searching codes are DARTS:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 1 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 1 -1
```

**After searching**, if you want to train the searched architecture found by the above scripts, please use the following codes:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-201/train-a-net.sh '|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|' 16 5
```
`|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|` represents the structure of a searched architecture. My codes will automatically print it during the searching procedure.


# Citation

If you find that this project helps your research, please consider citing the following paper:
```
@inproceedings{dong2019search,
  title     = {Searching for A Robust Neural Architecture in Four GPU Hours},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages     = {1761--1770},
  year      = {2019}
}
```