RandWireNN

Caveat: Currently, this repo have critical issue on random-graph generation. (See #8) This will be fixed, and experimented again. Since then, use JiaminRen's implementation.

Unofficial PyTorch Implementation of: Exploring Randomly Wired Neural Networks for Image Recognition.

Results

Validation result on Imagenet(ILSVRC2012) dataset:

Top 1 accuracy (%)	Paper	Here
RandWire-WS(4, 0.75), C=78	74.7	63.0

(2019.04.14) 62.6%: 396k steps with SGD optimizer, lr 0.1, momentum 0.9, weigth decay 5e-5, lr decay about 0.1 at 300k
(2019.04.12) 62.6%: 416k steps with Adabound optimizer, initial lr 0.001(decayed about 0.1 at 300k), final lr 0.1, no weight decay
JiaminRen's implementation reached accuarcy which is almost close to paper, using identical training strategy with paper.
(2019.04.10) 63.0%: 450k steps with Adam optimizer, initial lr 0.001, lr decay about 0.1 for every 150k step
(2019.04.07) 56.8%: Training took about 16 hours on AWS p3.2xlarge(NVIDIA V100). 120k steps were done in total, and Adam optimizer with lr=0.001, batch_size=128 was used with no learning rate decay.
Orange: Adam
Blue: AdaBound
Red: SGD

Dependencies

This code was tested on Python 3.6 with PyTorch 1.0.1. Other packages can be installed by:

pip install -r requirements.txt

Generate random DAG

cd model/graphs
python er.py -p 0.2 -o er-02.txt # Erdos-Renyi
python ba.py -m 7 -o ba-7.txt # Barbasi-Albert
python ws.py -k 4 -p 0.75 ws-4-075.txt # Watts-Strogatz
# number of nodes: -n option

All outputs from commands shown above will produce txt file like:

(number of nodes)
(number of edges)
(lines, each line representing edges)

Train RandWireNN

Download ImageNet dataset. Train/val folder should contain list of 1,000 directories, each containing list of images for corresponding category. For validation image files, this script can be useful: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh

Edit config.yaml

cd config
cp default.yaml config.yaml
vim config.yaml # specify data directory, graph txt files

Train

Note. Validation performed here won't use entire test set, since it will consume much time. (about 3 min.)
```
python trainer.py -c [config yaml] -m [name]
```
View tensorboardX
```
tensorboard --logdir ./logs
```

Validation

Run full validation:

python validation.py -c [config path] -p [checkpoint path]

This will show accuracy and average test loss of the trained model.

Author

Seungwon Park / @seungwonpark

License

Apache License 2.0

ghostlyFeng/RandWireNN