Go to data/ folder and run python2 generate_cifar10_tfrecords.py --data-dir=./cifar-10-data. This code is directly borrowed from tensorflow official repo and have to be run with python 2.7+.
Train
Use default parameters:
python main.py
Check out tunable hyper-parameters:
python main.py --help
Other parameters including stages, groups, condense factor, and growth rate are in experiment.py.
Notes
Training for 300 epochs with the default settings reach testing accuracy 93.389% (paper report is 94.94%). There might be some details I didn't notice, feel free to point them out.
All the default parameters settings follow the paper/official pytorch implementation.
Current implmentations of standard group convolution and learned group convolution are very inefficient (a bunch of reshape, transpose and concat), looking for help to build much more efficient graph.
Evaluation phase (index select) has not been implemented yet, looking for potential help as well :D.