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import torch
import torch.nn as nn
import numpy as np
class Hack_no_grad(nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, *inputs, **kwargs):
with torch.no_grad():
return self.module(*inputs, **kwargs)
def find_max_subspans(sequence, n_spans, max_length):
length = len(sequence)
inner_scores = np.zeros((length, n_spans + 1, max_length + 1, 2))
trace = np.zeros((length, n_spans + 1, max_length + 1, 2, 3), dtype=int)
# trace[:, n_spans, max_length, 0] = (n_spans, max_length, 0)
inner_scores[-1, :, :, 1] = -1e5
for _i in range(length):
for _j in range(n_spans+1):
for _k in range(max_length+1):
trace[_i, _j, _k, 0] = (_j, max_length, 0)
for _i in range(length):
for _j in range(n_spans):
for _k in range(max_length+1):
inner_scores[_i, _j, _k, 0], trace[_i, _j, _k, 0] = (
inner_scores[_i-1, _j, max_length, 0],
(_j, max_length, 0)
)
max_taken = inner_scores[_i-1, _j, :, 1].max()
if max_taken > inner_scores[_i, _j, _k, 0]:
inner_scores[_i, _j, _k, 0] = max_taken
trace[_i, _j, _k, 0] = (
_j, inner_scores[_i-1, _j, :, 1].argmax(), 1)
if _k < max_length:
inner_scores[_i, _j, _k, 1], trace[_i, _j, _k, 1] = (
(
inner_scores[_i-1, _j, _k+1, 1] + sequence[_i],
(_j, _k+1, 1)
)
if (inner_scores[_i-1, _j, _k+1, 1] >
inner_scores[_i-1, _j+1, max_length, 0])
else (
inner_scores[_i-1, _j+1, max_length, 0] +
sequence[_i],
(_j+1, max_length, 0)
)
)
max_score = 0
argmax = (0, 0, 0)
for _j in reversed(range(n_spans + 1)):
for _k in reversed(range(max_length)):
if inner_scores[-1, _j, _k, 0] > max_score:
max_score = inner_scores[-1, _j, _k, 0]
argmax = (_j, _k, 0)
if inner_scores[-1, _j, _k, 1] > max_score:
max_score = inner_scores[-1, _j, _k, 1]
argmax = (_j, _k, 1)
trace_back = argmax
tags = []
for _i in reversed(range(length)):
tags.append(trace_back[2])
trace_back = trace[_i, trace_back[0], trace_back[1], trace_back[2]]
tags.reverse()
segments = []
start = None
for _i in range(length + 1):
if _i < length and tags[_i] == 1 and start is None:
start = _i
elif (_i == length or tags[_i] == 0) and start is not None:
segments.append((start, _i))
start = None
return segments, max_score, tags # , inner_scores, trace
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