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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) | |