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

Last update: Dec 20, 2022

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:

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.

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

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

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

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

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

3 Aug 3, 2022

[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

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

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

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

10 May 2, 2022

Comments

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?

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)

v1.0.0(Aug 3, 2021)

First Official Release
Source code(tar.gz)
Source code(zip)
v0.1.0(Jun 23, 2021)

Source code(tar.gz)
Source code(zip)

Owner

Keon Lee

GitHub Repository

An easy-to-use app to visualise attentions of various VQA models.

Ask Me Anything: A tool for visualising Visual Question Answering (AMA) An easy-to-use app to visualise attentions of various VQA models. Please click

37 Nov 13, 2022

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD

220 Dec 26, 2022

Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks

LMMNN Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks This is the working dire

10 Nov 02, 2022

Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

Self-Tuning for Data-Efficient Deep Learning This repository contains the implementation code for paper: Self-Tuning for Data-Efficient Deep Learning

101 Dec 11, 2022

This is the PyTorch implementation of GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation

Official PyTorch repo for GAN's N' Roses. Diverse im2im and vid2vid selfie to anime translation.

1.1k Jan 01, 2023

Detector for Log4Shell exploitation attempts

log4shell-detector Detector for Log4Shell exploitation attempts Idea The problem with the log4j CVE-2021-44228 exploitation is that the string can be

729 Dec 25, 2022

Python implementation of "Single Image Haze Removal Using Dark Channel Prior"

##Dependencies pillow(~2.6.0) Numpy(~1.9.0) If the scripts throw AttributeError: __float__, make sure your pillow has jpeg support e.g. try: $ sudo ap

73 Dec 20, 2022

Lightweight library to build and train neural networks in Theano

Lasagne Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as C

3.8k Dec 29, 2022

PyTorch implementation of the Transformer in Post-LN (Post-LayerNorm) and Pre-LN (Pre-LayerNorm).

Transformer-PyTorch A PyTorch implementation of the Transformer from the paper Attention is All You Need in both Post-LN (Post-LayerNorm) and Pre-LN (

22 Feb 27, 2022

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Exercises and project documentation for the 3. Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

1 Jan 13, 2022

How to Train a GAN? Tips and tricks to make GANs work

(this list is no longer maintained, and I am not sure how relevant it is in 2020) How to Train a GAN? Tips and tricks to make GANs work While research

10.8k Dec 31, 2022

An Efficient Implementation of Analytic Mesh Algorithm for 3D Iso-surface Extraction from Neural Networks

AnalyticMesh Analytic Marching is an exact meshing solution from neural networks. Compared to standard methods, it completely avoids geometric and top

45 Dec 21, 2022

Complementary Patch for Weakly Supervised Semantic Segmentation, ICCV21 (poster)

CPN (ICCV2021) This is an implementation of Complementary Patch for Weakly Supervised Semantic Segmentation, which is accepted by ICCV2021 poster. Thi

20 Dec 12, 2022

A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation

##A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation. #USAGE To run the trained classifier on some images: python w

13 Nov 17, 2022

N-Person-Check-Checker-Splitter - A calculator app use to divide checks

N-Person-Check-Checker-Splitter This is my from-scratch programmed calculator ap

2 Feb 15, 2022

The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

ASMA-GAN Anisotropic Stroke Control for Multiple Artists Style Transfer Proceedings of the 28th ACM International Conference on Multimedia The officia

146 Nov 21, 2022

Efficient Lottery Ticket Finding: Less Data is More

The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical subnetworks) for dense networks, that can be trained in isolation from random initialization to match

20 Sep 04, 2022

Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

Blacksmith Rowhammer Fuzzer This repository provides the code accompanying the paper Blacksmith: Scalable Rowhammering in the Frequency Domain that is

173 Nov 16, 2022

I tried to apply the CAM algorithm to YOLOv4 and it worked.

YOLOV4：You Only Look Once目标检测模型在pytorch当中的实现 2021年2月7日更新：加入letterbox_image的选项，关闭letterbox_image后网络的map得到大幅度提升。目录性能情况 Performance 实现的内容 Achievement

55 Dec 05, 2022

TensorFlow2 Classification Model Zoo playing with TensorFlow2 on the CIFAR-10 dataset.

Training CIFAR-10 with TensorFlow2(TF2) TensorFlow2 Classification Model Zoo. I'm playing with TensorFlow2 on the CIFAR-10 dataset. Architectures LeNe

16 Sep 27, 2022