F-Conv_MindSpore/MinistExp

• Usage of The Codes

Usage of The Codes

Folder structure:

Data\    : data folder
    |-- mnist_rotation_new.zip : roatated minist data, Unzip before use. 
Models\  : Trained result
    |-- NormalNet\      : Trained parameters
    |-- NormalNet_best_of_30_repetitions\     : Best model parameter of 30 random repetitions
    |-- SimpleNet\      : Trained parameters of the simple network in the paper
DataLoader.py       : The data reading and preparing code
F_Conv.py           : Core codes of the equivariant convolutions of F-Conv
MyLib.py            : Some used code
Rotated_MNIST_Main.py             : Main code of experiment
Rotated_MNIST_simpleCase_Main.py  : Main code of experiment of the simple case in the paper
SteerableCNN_XQ.py                : F-Conv based Networks 
Demo.bat                          : Demo for running all the experiments

Usage:

Unzip Data/mnist_rotation_new.zip to get the rotated mnist dataset first, which can also be download from http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_rotation_new.zip.

To run testing with the example data, you can just run Rotated_MNIST_Main.py which is equivariant under reflections call:

python Rotated_MNIST_Main.py --dir NormalNet

To test the best model trained by us, one can call:

python Rotated_MNIST_Main.py --dir NormalNet_best_of_30_repetitions

To train the model, one can call:

python Rotated_MNIST_Main.py --dir retrain --mode train

To test and train the simple cases, one call:

python Rotated_MNIST_simpleCase_Main.py --dir SimpleNet 
python Rotated_MNIST_simpleCase_Main.py --dir SimpleNet_Retrain --mode train

Citation:

Qi Xie, Qian Zhao, Zongben Xu and Deyu Meng*. 
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions[J]. 
IEEE transactions on pattern analysis and machine intelligence, 2022.

BibTeX:

@article{xie2020MHFnet,
title={Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions},
author={Xie, Qi and Zhao, Qian and Xu, Zongben and Meng, Deyu},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}