dtw-loss-functions 1.0.4

Torch implementation of various DTW inpsired loss functions

DTW Inspired Loss Functions

Python package with the implementation of various DTW-inspired loss functions. Each implementation is compatible with PyTorch and can be used for training.

Loss Functions Currently Implemented

• SDTW. CUDA implementation of the SofDTW algorithm. This package offers several implementations of this algorithm. Currently
  • pytorch-softdtw-cuda by Mehran Maghoumi
  • pysdtw by Antoine Loriette
  • sdtw-cuda-torch by BGU-CS-VIL (implemented by Ron Shapira Weber)
• BlockDTW. Alternative version of SDTW, where a block-wise computation is used to improve performance. See the paper BlockDTW: Efficient and Scalable Similarity Search Algorithm for Healthcare-Focused Time-Series for more details.
• OTW. Implementation of the Optimal Transport Warping function. See the paper OTW: Optimal Transport Warping for Time Series for more details.

Documentation

You can read the package documentation at the link

Note that for now the documentation is generated automatically from the docstrings in the code, through sphinx autodoc. It will be improved in the future, with more examples and explanations.

Important Notes about Repository Name

The original name of this repository was block-sdtw because it was initially created to develop only the code related to block-DTW loss function. As with many academic projects, the code was initially "not very well organized", so I decided to refactor it and transform it into a Python package for easier use. Since my implementation of Block-DTW relies on Mehran Maghoumi's implementation of SoftDTW, I decided to also include SoftDTW inside the package. Due to some testing, I also had the opportunity to implement the OTW loss function.

At this point, given the presence of 3 different loss functions within the code base, the name block-sdtw for the package seemed a bit misleading to me. So, I decided to change the name from block-sdtw to dtw-loss-function.

Installation

pip installation

The easiest way to use this package is to install it via pip

pip install dtw-loss-function

Build from source

Alternatively, you can download the repository and compile it locally via hatchling

pip install hatchling
git clone https://github.com/jesus-333/dtw_loss_functions.git
cd dtw_loss_functions
hatchling build && pip install .

Usage Examples

Each loss function is implemented as a class inside the package.

Block-DTW Example

import torch
from dtw_loss_functions import block_dtw

block_size = 25

use_cuda = torch.cuda.is_available()
device = 'cuda' if use_cuda else 'cpu'

batch_size = 5
time_samples = 300
channels = 1

x   = torch.randn(batch_size, time_samples, channels).to(device)
x_r = torch.randn(batch_size, time_samples, channels).to(device)

block_dtw_loss = block_dtw.block_dtw(block_size, use_cuda)

output_block_dtw = block_dtw_loss(x, x_r)

SoftDTW Example

import torch
from dtw_loss_functions import soft_dtw

use_cuda = torch.cuda.is_available()
device = 'cuda' if use_cuda else 'cpu'

batch_size = 5
time_samples = 300
channels = 1

x   = torch.randn(batch_size, time_samples, channels).to(device)
x_r = torch.randn(batch_size, time_samples, channels).to(device)

sdtw_loss = soft_dtw.soft_dtw(use_cuda = use_cuda)

output_sdtw = sdtw_loss(x, x_r)

Citation

If you use this package refer to the citation file for all the info regarding the works to cite.