pysdtw

Torch implementation of the Soft-DTW algorithm, supports both cpu and CUDA hardware.

Note: This repository started as a fork from this project.

Installation

This package is available on pypi and depends on pytorch and numba.

Install with:

pip install pysdtw

Usage

import pysdtw

# the input data includes a batch dimension
X = torch.rand((10, 5, 7), requires_grad=True)
Y = torch.rand((10, 9, 7))

# optionally choose a pairwise distance function
fun = pysdtw.distance.pairwise_l2_squared

# create the SoftDTW distance function
sdtw = pysdtw.SoftDTW(gamma=1.0, dist_func=fun, use_cuda=False)

# soft-DTW discrepancy, approaches DTW as gamma -> 0
res = sdtw(X, Y)

# define a loss, which gradient can be backpropagated
loss = res.sum()
loss.backward()

# X.grad now contains the gradient with respect to the loss