https://github.com/EmilienDupont/augmented-neural-odes

Pytorch implementation of Augmented Neural ODEs :sunflower:
https://github.com/EmilienDupont/augmented-neural-odes

Last synced: 9 days ago
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Pytorch implementation of Augmented Neural ODEs :sunflower:

• Host: GitHub
• URL: https://github.com/EmilienDupont/augmented-neural-odes
• Owner: EmilienDupont
• License: mit
• Created: 2019-05-19T17:55:17.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-02-09T11:55:18.000Z (almost 2 years ago)
• Last Synced: 2024-08-01T13:25:32.509Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 5.57 MB
• Stars: 525
• Watchers: 19
• Forks: 89
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Augmented Neural ODEs

This repo contains code for the paper [Augmented Neural ODEs](https://arxiv.org/abs/1904.01681) (2019).

## Examples



## Requirements

The requirements that can be directly installed from PyPi can be found in `requirements.txt`. This code also builds on the awesome `torchdiffeq` library, which provides various ODE solvers on GPU. Instructions for installing `torchdiffeq` can be found in this [repo](https://github.com/rtqichen/torchdiffeq).

## Usage

The usage pattern is simple:

```python

# ... Load some data ...

import torch

from anode.conv_models import ConvODENet

from anode.models import ODENet

from anode.training import Trainer

# Instantiate a model

# For regular data...

anode = ODENet(device, data_dim=2, hidden_dim=16, augment_dim=1)

# ... or for images

anode = ConvODENet(device, img_size=(1, 28, 28), num_filters=32, augment_dim=1)

# Instantiate an optimizer and a trainer

optimizer = torch.optim.Adam(anode.parameters(), lr=1e-3)

trainer = Trainer(anode, optimizer, device)

# Train model on your dataloader

trainer.train(dataloader, num_epochs=10)

```

More detailed examples and tutorials can be found in the `augmented-neural-ode-example.ipynb` and `vector-field-visualizations.ipynb` notebooks.

### Running experiments

To run a large number of repeated experiments on toy datasets, use the following

```

python main_experiment.py config.json

```

where the specifications for the experiment can be found in `config.json`. This will log all the information about the experiments and generate plots for losses, NFEs and so on.

### Running experiments on image datasets

To run large experiments on image datasets, use the following

```

python main_experiment_img.py config_img.json

```

where the specifications for the experiment can be found in `config_img.json`.

## Demos

We also provide two demo notebooks that show how to reproduce some of the results and figures from the paper.

### Vector fields



The `vector-field-visualizations.ipynb` notebook contains a demo and tutorial for reproducing the experiments on 1D ODE flows in the paper.

### Augmented Neural ODEs



The `augmented-neural-ode-example.ipynb` notebook contains a demo and tutorial for reproducing the experiments comparing Neural ODEs and Augmented Neural ODEs on simple 2D functions.

## Data

The MNIST and CIFAR10 datasets can be directly downloaded using `torchvision` (this will happen automatically if you run the code, unless you already have those datasets downloaded). To run experiments on ImageNet, you will need to download the data from the [Tiny ImageNet](https://tiny-imagenet.herokuapp.com/) website.

## Citing

If you find this code useful in your research, consider citing with

```

@article{dupont2019augmented,

  title={Augmented Neural ODEs},

  author={Dupont, Emilien and Doucet, Arnaud and Teh, Yee Whye},

  journal={arXiv preprint arXiv:1904.01681},

  year={2019}

}

```

## License

MIT