Source code for "Deep Unitary Convolutional Neural Networks."

"""
Released under BSD 3-Clause License,
Modifications are Copyright (c) 2021 Hao-Yuan Chang
All rights reserved.

== BSD 3-Clause License ==

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"""

==== Enviroment Setup ====

1. Download and install anaconda
    https://docs.anaconda.com/anaconda/install/
2. Create a new conda enviroment
    conda create -n torch anaconda python=3.8
3. Install PyTorch
    conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
4. Install required conda packages
    conda install --file requirements.txt

==== Running the Simulation ====

1. Train a unitary neural network for image recognition by running cifar10_main.py with these minimun amount of command line parameters:
    python cifar10_main.py <PATH TO CIFAR-10 DATASET> --logdir <PATH TO LOG DIRECTORY> --name <NAME OF THE EXPERIEMENT> --gpu <THE DEVICE ID OF THE GPU> --model <DEPTH OF THE NETWORK>
Full Example:
    python cifar10_main.py ~/dataset/cifar10 --logdir ~/test --name test --gpu 0 --model resnet44
2. Other normalization type reported in the paper can be reproducted by changing the --norm-mode switch.
Possible choices: 
    ['batch', 'group', 'layer', 'instance', 'none','unitary']