This repo contains a reference implementation of our proposed declarative DTW layer named DecDTW (ICLR 2023). The repo contains a full implementation of our DecDTW layer as well as training/eval pipelines for the two experiments presented in the paper.
Make to the base directory of this repo your current working directory. Then run
pip install -r requirements.txt
pip install -e .
Sample usage:
import torch
from decdtw.utils import BatchedSignal
from decdtw.decdtw import DecDTWLayer
reg_lmbda = 0.1
decdtw = DecDTWLayer(subseq_enabled=True)
x = torch.randn(1, 8, 1) # BxNxD
y = torch.randn(1, 5, 1) # BxMxD
tx = torch.linspace(0., 1., 8).unsqueeze(0) # BxN
ty = torch.linspace(0., 1., 5).unsqueeze(0) # BxM
sx = BatchedSignal(x, times=tx)
sy = BatchedSignal(y, times=ty)
warp_fn = decdtw.forward(sx, sy, reg_lmbda) # output is a BatchedSignal
warp_fn.values # BxM
warp_fn.times # BxM
warp_fn is the predicted piecewise-linear warp function using GDTW. warp_fn.values is differentiable w.r.t. x and y and contains the warp function values at knot points specified by warp_fn.times.
See experiments/audio_score/README.md for detailed setup information of the underlying datasets and terminal commands to run training/eval.
See experiments/vpr/README.md for detailed setup information of the underlying datasets and terminal commands to run training/eval.
Please consider citing our paper if you find our work helpful for your research
@inproceedings{
xu2023deep,
title={Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths},
author={Ming Xu and Sourav Garg and Michael Milford and Stephen Gould},
booktitle={International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=UClBPxIZqnY}
}