Example fails on GPU

When running the example from the README:

from soft_dtw_cuda import SoftDTW
import torch
# 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()

I get a TypeError:

Traceback (most recent call last):
  File "test_dtw.py", line 10, in <module>
    loss = sdtw(x, y)  # Just like any torch.nn.xyzLoss()
  File "/home/dockeruser/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/dockeruser/soft_dtw_cuda.py", line 352, in forward
    return func_dtw(D_xy, self.gamma, self.bandwidth)
  File "/home/dockeruser/soft_dtw_cuda.py", line 140, in forward
    compute_softdtw_cuda[B, threads_per_block](cuda.as_cuda_array(D.detach()),
  File "/home/dockeruser/env/lib/python3.7/site-packages/numba/cuda/api.py", line 74, in as_cuda_array
    raise TypeError("*obj* doesn't implement the cuda array interface.")
TypeError: *obj* doesn't implement the cuda array interface.

Potentially relevant packages from my env: Cython 0.29.24 numba 0.54.0 torch 1.6.0

Any suggestions?

BTW. Thanks a lot for porting to cuda!

[Yutaro-Sanada]

I also encountered. However, after inserting two lines before sdtw = SoftDTW(use_cuda=True, gamma=0.1), I can run this example.

x = x.cuda()
y = y.cuda()

[Maghoumi]

#17 is now merged. Closing this issue.