| | |
| |
|
| | import numpy as np |
| | import torch |
| | from .monotonic_align.core import maximum_path_c |
| |
|
| |
|
| | def maximum_path(neg_cent, mask): |
| | """Cython optimized version. |
| | neg_cent: [b, t_t, t_s] |
| | mask: [b, t_t, t_s] |
| | """ |
| | device = neg_cent.device |
| | dtype = neg_cent.dtype |
| | neg_cent = neg_cent.data.cpu().numpy().astype(np.float32) |
| | path = np.zeros(neg_cent.shape, dtype=np.int32) |
| |
|
| | t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32) |
| | t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32) |
| | maximum_path_c(path, neg_cent, t_t_max, t_s_max) |
| | return torch.from_numpy(path).to(device=device, dtype=dtype) |
| |
|
