PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA

Related tags

Deep Learningdeep-neural-networksdeep-learningtime-seriesdtwcudapytorchdynamic-time-warpingsoft-dtwloss-function
Overview

Soft DTW Loss Function for PyTorch in CUDA

This is a Pytorch Implementation of Soft-DTW: a Differentiable Loss Function for Time-Series which is batch supported computation, CUDA-friendly, and feasible to use as a final loss. I can confirm that you can train a (sequential) model with this as a final loss! The following image shows training logs of a TTS model using the Soft-DTW Loss Function.

There are some previous implementations:

  1. mblondel's soft-dtw
  2. lyprince's sdtw_pytorch
  3. Maghoumi's pytorch-softdtw-cuda

But they are either not supported by CUDA-friendly batch computation or not considering the jacobean w.r.t input matrix, which is necessary to be used as a final loss in recent deep learning frameworks. In the current implementation, all conditions are satisfied.

Usage

Same as Maghoumi's pytorch-softdtw-cuda:

from sdtw_cuda_loss import SoftDTW

# Create the sequences
batch_size, len_x, len_y, dims = 8, 15, 12, 5
x = torch.rand((batch_size, len_x, dims), requires_grad=True)
y = torch.rand((batch_size, len_y, dims))

# Create the "criterion" object
sdtw = SoftDTW(use_cuda=True, gamma=0.1)

# Compute the loss value
loss = sdtw(x, y)  # Just like any torch.nn.xyzLoss()

# Aggregate and call backward()
loss.mean().backward()

But the backward will compute the gradient w.r.t input target sequence x (which is not considered in the previous work).

Note

In the current implementation, only use_cuda=True is supported. But you can easily implement the CPU version as in Maghoumi's pytorch-softdtw-cuda.

Citation

@misc{lee2021soft_dtw_loss,
  author = {Lee, Keon},
  title = {Soft-DTW-Loss},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/Soft-DTW-Loss}}
}
You might also like...
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

Peidong Liu(刘沛东) 54 Dec 17, 2022
Multi-scale discriminator feature-wise loss function

Multi-Scale Discriminative Feature Loss This repository provides code for Multi-Scale Discriminative Feature (MDF) loss for image reconstruction algor

Graphics and Displays group - University of Cambridge 76 Dec 12, 2022
clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

README clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation CVPR 2021 Authors: Suprosanna Shit and Johannes C. Paetzo

null 110 Dec 29, 2022
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images Histological Image Segmentation This

Saad Wazir 11 Dec 16, 2022
Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation

SSWS-loss_function_based_on_MS-TCN Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation Supervised Sliding Window

null 3 Aug 3, 2022
[CVPR 2022] Official code for the paper:
[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

MDCA Calibration This is the official PyTorch implementation for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved

MDCA Calibration 21 Dec 22, 2022
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

null 20 Oct 24, 2022
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

Softlearning Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is

Robotic AI & Learning Lab Berkeley 997 Dec 30, 2022
Decorators for maximizing memory utilization with PyTorch & CUDA

torch-max-mem This package provides decorators for memory utilization maximization with PyTorch and CUDA by starting with a maximum parameter size and

Max Berrendorf 10 May 2, 2022
Comments
  • Does this supports multi-gpu training?

    Does this supports multi-gpu training?

    Thanks for sharing impl of soft-dtw, I can use it in single-gpu env,but can't use it in multi-gpu envs.Currently, it doesn't support multi-gpu training?

    opened by mayfool 2
  • how to use dtw-loss to fit a curve?

    how to use dtw-loss to fit a curve?

    hello, I tried to fit a curve (discrete points) using Soft-DTW-Loss as a loss function. But the loss does not converge to the exact result in the end. Is there something wrong with the way I am using it? The code is as follows:

    if name == "main":

    batch_size = 1
len_x = 15
len_predict = 10
dims = 1

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

x = torch.unsqueeze(torch.linspace(1, 4, steps=len_x, requires_grad=True), dim=0)
y = x ** 2
y = y.view(1, len_x, 1)
x = x.view(1, len_x, 1)

#(batch,length,dims)---->(1,15,2)
truth_points = torch.cat((y, x), dim=2).cuda()

#(1,20)
input = torch.unsqueeze(torch.linspace(1, 4, steps=len_predict*2, requires_grad=True), dim=0).cuda()


class testNN(torch.nn.Module):
    def __init__(self):
        super(testNN, self).__init__()
        self.layer = nn.Sequential(
            nn.Linear(20, 50),
            nn.ReLU(),
            nn.Linear(50, 200),
            nn.ReLU(),
            nn.Linear(200, 50),
            nn.ReLU(),
            nn.Linear(50, 20),
            nn.ReLU(),
        )
    def forward(self, x):
        x = self.layer(x)
        return x


test = testNN()
test = test.to(device)

loss_function = SoftDTW(use_cuda=True, gamma=0.01, normalize=False)
optimizer = torch.optim.Adam(test.parameters(), lr=0.01)


for epoch in range(1000):


    predict = test(input)
    #(1,20) reshape to (1,10,2)
    predict = predict.reshape(1, len_predict, 2)
    loss = loss_function(predict, truth_points)
    optimizer.zero_grad()
    loss.mean().backward(retain_graph=True)
    optimizer.step()


    if epoch % 10 == 0:
        print("epoch : %d | loss : %f" % (epoch, loss))
        plt_predict = predict.cpu().detach().numpy()
        # print(plt_predict)
        plt_predict = plt_predict.reshape(1, len_predict, 2)
        print(plt_predict[0, :, 0])
        print(plt_predict[0, :, 1])
    opened by visionlyx 0
Releases(v1.0.0)
Owner
Keon Lee
Expressive Speech Synthesis | Conversational AI | Open-domain Dialog | NLP | Generative Models | Empathic Computing | HCI
Keon Lee
GitHub Repository
Using pretrained GROVER to extract the atomic fingerprints from molecule

Extracting atomic fingerprints from molecules using pretrained Graph Neural Network models (GROVER).

Xuan Vu Nguyen 1 Jan 28, 2022
Object Depth via Motion and Detection Dataset

ODMD Dataset ODMD is the first dataset for learning Object Depth via Motion and Detection. ODMD training data are configurable and extensible, with ea

Brent Griffin 172 Dec 21, 2022
Neuralnetwork - Basic Multilayer Perceptron Neural Network for deep learning

Neural Network Just a basic Neural Network module Usage Example Importing Module

andreecy 0 Nov 01, 2022
PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

PyTorch implementation of [1611.06440 Pruning Convolutional Neural Networks for Resource Efficient Inference] This demonstrates pruning a VGG16 based

Jacob Gildenblat 836 Dec 26, 2022
EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at

9 Oct 28, 2022
KAPAO is an efficient multi-person human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

KAPAO (Keypoints and Poses as Objects) KAPAO is an efficient single-stage multi-person human pose estimation model that models keypoints and poses as

Will McNally 664 Dec 30, 2022
Unsupervised Representation Learning by Invariance Propagation

Unsupervised Learning by Invariance Propagation This repository is the official implementation of Unsupervised Learning by Invariance Propagation. Pre

FengWang 15 Jul 06, 2022
[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Using Unreliable Pseudo Labels Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022. Ple

Haochen Wang 268 Dec 24, 2022
Convolutional 2D Knowledge Graph Embeddings resources

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

Tim Dettmers 586 Dec 24, 2022
Repository of best practices for deep learning in Julia, inspired by fastai

FastAI Docs: Stable | Dev FastAI.jl is inspired by fastai, and is a repository of best practices for deep learning in Julia. Its goal is to easily ena

FluxML 532 Jan 02, 2023
The open-source and free to use Python package miseval was developed to establish a standardized medical image segmentation evaluation procedure

miseval: a metric library for Medical Image Segmentation EVALuation The open-source and free to use Python package miseval was developed to establish

59 Dec 10, 2022
This is the source code of the solver used to compete in the International Timetabling Competition 2019.

ITC2019 Solver This is the source code of the solver used to compete in the International Timetabling Competition 2019. Building .NET Core (2.1 or hig

Edon Gashi 8 Jan 22, 2022
NVIDIA container runtime

nvidia-container-runtime A modified version of runc adding a custom pre-start hook to all containers. If environment variable NVIDIA_VISIBLE_DEVICES i

NVIDIA Corporation 938 Jan 06, 2023
PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Unsupervised_IEPGAN This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer. Ha

25 Oct 26, 2022
Creative Applications of Deep Learning w/ Tensorflow

Creative Applications of Deep Learning w/ Tensorflow This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for th

Parag K Mital 1.5k Dec 30, 2022
A variational Bayesian method for similarity learning in non-rigid image registration (CVPR 2022)

A variational Bayesian method for similarity learning in non-rigid image registration We provide the source code and the trained models used in the re

daniel grzech 14 Nov 21, 2022
A simple code to convert image format and channel as well as resizing and renaming multiple images.

Rename-Resize-and-convert-multiple-images A simple code to convert image format and channel as well as resizing and renaming multiple images. This cod

Happy N. Monday 3 Feb 15, 2022
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer

Multimedia Research 484 Dec 29, 2022
Erpnext app for make employee salary on payroll entry based on one or more project with percentage for all project equal 100 %

Project Payroll this app for make payroll for employee based on projects like project on 30 % and project 2 70 % as account dimension it makes genral

Ibrahim Morghim 8 Jan 02, 2023
Mercury: easily convert Python notebook to web app and share with others

Mercury Share your Python notebooks with others Easily convert your Python notebooks into interactive web apps by adding parameters in YAML. Simply ad

MLJAR 2.2k Dec 27, 2022