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Official PyTorch implementation for our NeurIPS 2019 paper, Diffeomorphic Temporal Alignment Nets. TensorFlow\Keras version is available at tf_legacy branch.

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## While this repo is the official implementation of Diffeomorphic Temporal Alignment Nets (DTAN), the method from our NeurIPS 2019 paper, we strongly encourage you to use the (much better) method from our ICML 2023 paper, [Regularization-free Diffeomorphic Temporal Alignment Nets](https://github.com/BGU-CS-VIL/RF-DTAN) (RF-DTAN). 
For one thing, RF-DTAN will save you the need to do hyper-parameter tuning, as it requires no regularization over the predicted transformations.

# Diffeomorphic Temporal Alignment Nets
## We moved to PyTorch! 
TensorFlow implementation (old master branch) is available at tf_legacy branch.

Repository for our NeurIPS 2019 paper, [Diffeomorphic Temporal Alignment Nets](https://www.cs.bgu.ac.il/~orenfr/DTAN/ShapiraWeber_NeurIPS_2019.pdf) co-authored by: Ron Shapira Weber, Matan Eyal, Nicki Skafte Detlefsen, Oren Shriki and Oren Freifeld.
<img src="/figures/dtan_intro_fig.png" alt="DTAN joint alignmnet of ECGFiveDays dataset.">
## Model Architecture
<img src="/figures/DTAN_detailed_model.png" alt="DTAN Architecture.">

## Author of this software
Ron Shapira Weber (email: ronsha@post.bgu.ac.il)

## Requirements
- Standard Python(>=3.6) packages: numpy, matplotlib, tqdm, seaborn
- PyTorch >= 1.4
- tslean == 0.5.2
- libcpab == 2.0
- For Nvidia GPU iimplementation: CUDA==11.0 + appropriate cuDNN as well. You can follow the instructions [here](https://pytorch.org/get-started/locally/).

## Operation system: 
For the native PyTorch implementation (slower), we support all operating systems. 
For the fast CUDA implementation of libcpab, we only support Linux.

## Installation
We recommend installing a [virtual environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands) via Anaconda.
For instance:
```
conda create -n dtan python=3.7 numpy matplotlib seaborn tqdm
```
### libcpab
licpab [2] is a python package supporting the CPAB transformations [1] in Numpy, Tensorflow and Pytorch.
For your convince, we have added a lightweight version of libcpab at DTAN/libcpab.

That being said, you are still encouraged to install the full package.

Install [libcpab](https://github.com/SkafteNicki/libcpab) 
Note 1: you might have to recompile the dynamic libraries under /libcpab/tensorflow/ 
```
git clone https://github.com/SkafteNicki/libcpab
```
Add libcpab to your python path:
```
export PYTHONPATH=$PYTHONPATH:$YOUR_FOLDER_PATH/libcpab
```
Make sure libcpab was installed properly (Run one of the demos).

### DTAN
Clone the repository:
```
git clone https://github.com/BGU-CS-VIL/dtan.git
# move to pytorch branch
git checkout pytorch
```
Add DTAN to your python path:
```
export PYTHONPATH=$PYTHONPATH:$YOUR_FOLDER_PATH/dtan
```
Try the example code under dtan/exmaples (see also our Usage section below):
```
python UCR_alignment.py
```
## Usage
### Examples
1. To initialize the model:

```python
from DTAN.DTAN_layer import DTAN as dtan_model

# Init model to GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = dtan_model(signal_len=int, channels=int, tess=[int,], n_recurrence=int,
                    zero_boundary=bool, device='gpu').to(device)
```

Under the 'examples' dir you can find example scripts for training and running DTAN time-series joint alignment.

2. **UCR time-series classification archive [3] alignment example.** 
To run, simply enter (from the examples dir):
```
python UCR_alignment.py
```
We support loading data via [tslearn](https://tslearn.readthedocs.io/en/stable/index.html) [4].

For more information regarding the UCR archive, please visit:
[https://www.cs.ucr.edu/~eamonn/time_series_data/](https://www.cs.ucr.edu/~eamonn/time_series_data/)

The script supports the following flags:
```
optional arguments:
  -h, --help            show this help message and exit
  --dataset UCR dataset name
                        string. Dataset name to load from tslearn.datasets.UCR_UEA_datasets
  --tess_size TESS_SIZE
                        CPA velocity field partition
  --smoothness_prior    smoothness prior flag
  --no_smoothness_prior
                        no smoothness prior flag
  --lambda_smooth LAMBDA_SMOOTH
                        lambda_smooth, larger values -> smoother warps
  --lambda_var LAMBDA_VAR
                        lambda_var, larger values -> larger warps
  --n_recurrences N_RECURRENCES
                        number of recurrences of R-DTAN
  --zero_boundary ZERO_BOUNDARY
                        zero boundary constrain
  --n_epochs N_EPOCHS   number of epochs
  --batch_size BATCH_SIZE
                        batch size
  --lr LR               learning rate

```

2. **Usage Example - Running with and without smoothness prior**: 
See the [jupyter notebook](https://github.com/BGU-CS-VIL/dtan/blob/pytorch/notebooks/Usage%20Example%20-%20Running%20with%20and%20without%20prior.ipynb), illustrating the importance of the smoothness prior.

3. **UCR Nearest Centroid Classification (NCC)**:
*Coming soon to PyTorch version* 
Here we provide an end-to-end pipeline for the NCC experiment described in our paper.
The script uses the same hyper-parameters (i.e., lambda_var, lambda_smooth, n_recurrences) used in the paper, depending on the UCR dataset. 
To run the pipeline on the 'ECGFiveDays' dataset go to the 'examples' dir and simply run:

```
python UCR_NCC.py
```
You can change the dataset inside the script.
## References
```
[1] @article{freifeld2017transformations,
  title={Transformations Based on Continuous Piecewise-Affine Velocity Fields},
  author={Freifeld, Oren and Hauberg, Soren and Batmanghelich, Kayhan and Fisher, John W},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2017},
  publisher={IEEE}
}

[2] @misc{detlefsen2018,
  author = {Detlefsen, Nicki S.},
  title = {libcpab},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/SkafteNicki/libcpab}},
}
[3] @misc{UCRArchive,
title={The UCR Time Series Classification Archive},
author={ Chen, Yanping and Keogh, Eamonn and Hu, Bing and Begum, Nurjahan and Bagnall, Anthony and Mueen, Abdullah and Batista, Gustavo},
year={2015},
month={July},
note = {\url{www.cs.ucr.edu/~eamonn/time_series_data/}}
}
[4] @article{JMLR:v21:20-091,
  author  = {Romain Tavenard and Johann Faouzi and Gilles Vandewiele and
             Felix Divo and Guillaume Androz and Chester Holtz and
             Marie Payne and Roman Yurchak and Marc Ru{\ss}wurm and
             Kushal Kolar and Eli Woods},
  title   = {Tslearn, A Machine Learning Toolkit for Time Series Data},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {118},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v21/20-091.html}
}
```
## Versions:

## License
This software is released under the MIT License (included with the software). Note, however, that if you are using this code (and/or the results of running it) to support any form of publication (e.g., a book, a journal paper, a conference paper, a patent application, etc.) then we request you will cite our paper:
```
@inproceedings{weber2019diffeomorphic,
  title={Diffeomorphic Temporal Alignment Nets},
  author={Weber, Ron A Shapira and Eyal, Matan and Skafte, Nicki and Shriki, Oren and Freifeld, Oren},
  booktitle={Advances in Neural Information Processing Systems},
  pages={6570--6581},
  year={2019}
}

```

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