|
|
"""
|
|
|
Monotonic alignment package
|
|
|
"""
|
|
|
|
|
|
import numba
|
|
|
from numpy import zeros, int32, float32
|
|
|
from torch import from_numpy
|
|
|
|
|
|
|
|
|
@numba.jit(
|
|
|
numba.void(
|
|
|
numba.int32[:, :, ::1],
|
|
|
numba.float32[:, :, ::1],
|
|
|
numba.int32[::1],
|
|
|
numba.int32[::1],
|
|
|
),
|
|
|
nopython=True,
|
|
|
nogil=True,
|
|
|
)
|
|
|
def maximum_path_jit(paths, values, t_ys, t_xs):
|
|
|
b = paths.shape[0]
|
|
|
max_neg_val = -1e9
|
|
|
for i in range(int(b)):
|
|
|
path = paths[i]
|
|
|
value = values[i]
|
|
|
t_y = t_ys[i]
|
|
|
t_x = t_xs[i]
|
|
|
|
|
|
v_prev = v_cur = 0.0
|
|
|
index = t_x - 1
|
|
|
|
|
|
for y in range(t_y):
|
|
|
for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
|
|
|
if x == y:
|
|
|
v_cur = max_neg_val
|
|
|
else:
|
|
|
v_cur = value[y - 1, x]
|
|
|
if x == 0:
|
|
|
if y == 0:
|
|
|
v_prev = 0.0
|
|
|
else:
|
|
|
v_prev = max_neg_val
|
|
|
else:
|
|
|
v_prev = value[y - 1, x - 1]
|
|
|
value[y, x] += max(v_prev, v_cur)
|
|
|
|
|
|
for y in range(t_y - 1, -1, -1):
|
|
|
path[y, index] = 1
|
|
|
if index != 0 and (
|
|
|
index == y or value[y - 1, index] < value[y - 1, index - 1]
|
|
|
):
|
|
|
index = index - 1
|
|
|
|
|
|
|
|
|
def maximum_path(neg_cent, mask):
|
|
|
device = neg_cent.device
|
|
|
dtype = neg_cent.dtype
|
|
|
neg_cent = neg_cent.data.cpu().numpy().astype(float32)
|
|
|
path = zeros(neg_cent.shape, dtype=int32)
|
|
|
|
|
|
t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
|
|
|
t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
|
|
|
maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
|
|
|
return from_numpy(path).to(device=device, dtype=dtype)
|
|
|
|
