| 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) | |