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

Validation result on Imagenet(ILSVRC2012) dataset:

• (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.

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

pip install -r requirements.txt

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)

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

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
   

Run full validation:

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

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

Seungwon Park / @seungwonpark

Apache License 2.0