| from numpy import zeros, int32, float32 |
| from torch import from_numpy |
|
|
| from .core import maximum_path_jit |
|
|
|
|
| def maximum_path(neg_cent, mask): |
| """ numba 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(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) |
|
|
