Unofficial PyTorch Implementation of: Exploring Randomly Wired Neural Networks for Image Recognition.
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=128was used with no learning rate decay.
- Orange: Adam
- Blue: AdaBound
- Red: SGD
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)
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
cd config cp default.yaml config.yaml vim config.yaml # specify data directory, graph txt files
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]
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