|
|
|
|
|
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) |
|
|
