Python
Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

RandWireNN

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

Results

Currently, training is in progress.

Validation result on Imagenet(ILSVRC2012) dataset:

• (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.
• I plan to try:
  • Learning rate decay for every 150k step, by 0.1
  • AdaBound optimizer
  • Honestly, I'm not planning to try Distributed SGD, which was used in paper.
  • Any Pull Request reporting result with other training strategy will be welcome. Please let me know in prior if you're planning to do so.

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

1. 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
2. Edit config.yaml

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

1. 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]

1. 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