| | """
|
| | 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)
|
| |
|
