RandWireNN

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 | 69.2 |

• (2019.06.26) 69.2%: 250 epoch with SGD optimizer, lr 0.1, momentum 0.9, weight decay 5e-5, cosine annealing lr schedule (no label smoothing applied, see loss curve below)

• (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

• (2019.04) 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. 

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-Renyipython ba.py -m 7 -o ba-7.txt # Barbasi-Albertpython 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 bash cd config cp default.yaml config.yaml vim config.yaml # specify data directory, graph txt files

3. 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]

4. 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