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}}
}

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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

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