This repository contains pytorch implementation for our AAAI 2024 paper:
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations
Run the following code before starting the experiment.
conda env create -f requirements.yaml
Run the following code for training / test.
python main.py --tol 1e-3 --epochs 10 --batch_size 64 --hidden_size 76
If you want to train and test in a different environmental setting, it can be done by changing the parsers below.
[ parser ] [ Description of parser ]
--tol : DOPRI-5 error tolerance
--epochs : Number of epoch
--batch_size : Batch size
--hidden_size : Size of hidden vector
We release code for image classification tasks (CIFAR-10 dataset).
If you want to evaluate it quickly, run the following code :
python test.py --path './model/10.pt'
[ parser ] [ Description of parser ]
--path : The path where checkpoint exists
[ code ] [ Description of code ]
main.py : Code for training and testing.
models.py : Our model
utils.py : Modules required during training
test.py : Code for testing.
We provide checkpoint of trained BFNO-NODE(our model), which are located in the ./model folder.
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