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RandWireNN

PWC

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

Results

Validation result on Imagenet(ILSVRC2012) dataset:

Top 1 accuracy (%)PaperHere
RandWire-WS(4, 0.75), C=7874.769.2

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
    
  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