from numpy import zeros, int32, float32 from torch import from_numpy from .core import maximum_path_jit 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)