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File size: 641 Bytes
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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)
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