Maghoumi/pytorch-softdtw-cuda
Fast CUDA implementation of (differentiable) soft dynamic time warping for PyTorch
This helps deep learning researchers and practitioners using PyTorch to efficiently compare time series data, particularly for tasks like gesture recognition or motion analysis. It takes two batches of time series sequences as input and outputs a differentiable similarity score, much like a loss function. This tool is for machine learning engineers and researchers working with sequential data who need faster computation of dynamic time warping.
728 stars. No commits in the last 6 months.
Use this if you are working with time series data in PyTorch and need to compute Soft Dynamic Time Warping distances much faster than CPU-based implementations, especially for larger datasets and longer sequences.
Not ideal if your input sequences are extremely long (over 1024 data points) or if you are not using PyTorch and CUDA for your deep learning models.
Stars
728
Forks
66
Language
Python
License
MIT
Category
Last pushed
Apr 03, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Maghoumi/pytorch-softdtw-cuda"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lmcinnes/umap
Uniform Manifold Approximation and Projection
pyRiemann/pyRiemann
Machine learning for multivariate data through the Riemannian geometry of positive definite...
geomstats/geomstats
Computations and statistics on manifolds with geometric structures.
higra/Higra
Hierarchical Graph Analysis
pavlin-policar/openTSNE
Extensible, parallel implementations of t-SNE