Spaces:
Runtime error
Runtime error
File size: 7,480 Bytes
24d0437 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
from functools import lru_cache
from itertools import chain
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
def enumerate_spans(n):
for i in range(n):
for j in range(i, n):
yield (i, j)
@lru_cache # type: ignore
def get_all_spans(n: int) -> torch.Tensor:
return torch.tensor(list(enumerate_spans(n)), dtype=torch.long)
class SpanClassifier(nn.Module):
num_additional_labels = 1
def __init__(self, encoder, scorer: "SpanScorer"):
super().__init__()
self.encoder = encoder
self.scorer = scorer
def forward(
self, *input_ids: Sequence[torch.Tensor]
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
hs, lengths = self.encoder(*input_ids)
spans = list(map(get_all_spans, lengths))
scores = self.scorer(hs, spans)
return spans, scores
@torch.no_grad()
def decode(
self,
spans: Sequence[torch.Tensor],
scores: Sequence[torch.Tensor],
) -> List[List[Tuple[int, int, int]]]:
spans_flatten = torch.cat(spans)
scores_flatten = torch.cat(scores)
assert len(spans_flatten) == len(scores_flatten)
labels_flatten = scores_flatten.argmax(dim=1).cpu()
mask = labels_flatten < self.scorer.num_labels - 1
mentions = torch.hstack((spans_flatten[mask], labels_flatten[mask, None]))
output = []
offset = 0
sizes = [m.sum() for m in torch.split(mask, [len(idxs) for idxs in spans])]
for size in sizes:
output.append([tuple(m) for m in mentions[offset : offset + size].tolist()])
offset += size
return output # type: ignore
def compute_metrics(
self,
spans: Sequence[torch.Tensor],
scores: Sequence[torch.Tensor],
true_mentions: Sequence[Sequence[Tuple[int, int, int]]],
decode=True,
) -> Dict[str, Any]:
assert len(spans) == len(scores) == len(true_mentions)
num_labels = self.scorer.num_labels
true_labels = []
for spans_i, scores_i, true_mentions_i in zip(spans, scores, true_mentions):
assert len(spans_i) == len(scores_i)
span2idx = {tuple(s): idx for idx, s in enumerate(spans_i.tolist())}
labels_i = torch.full((len(spans_i),), fill_value=num_labels - 1)
for (start, end, label) in true_mentions_i:
idx = span2idx.get((start, end))
if idx is not None:
labels_i[idx] = label
true_labels.append(labels_i)
scores_flatten = torch.cat(scores)
true_labels_flatten = torch.cat(true_labels).to(scores_flatten.device)
assert len(scores_flatten) == len(true_labels_flatten)
loss = F.cross_entropy(scores_flatten, true_labels_flatten)
accuracy = categorical_accuracy(scores_flatten, true_labels_flatten)
result = {"loss": loss, "accuracy": accuracy}
if decode:
pred_mentions = self.decode(spans, scores)
tp, fn, fp = 0, 0, 0
for pred_mentions_i, true_mentions_i in zip(pred_mentions, true_mentions):
pred, gold = set(pred_mentions_i), set(true_mentions_i)
tp += len(gold & pred)
fn += len(gold - pred)
fp += len(pred - gold)
result["precision"] = (tp, tp + fp)
result["recall"] = (tp, tp + fn)
result["mentions"] = pred_mentions
return result
@torch.no_grad()
def categorical_accuracy(
y: torch.Tensor, t: torch.Tensor, ignore_index: Optional[int] = None
) -> Tuple[int, int]:
pred = y.argmax(dim=1)
if ignore_index is not None:
mask = t == ignore_index
ignore_cnt = mask.sum()
pred.masked_fill_(mask, ignore_index)
count = ((pred == t).sum() - ignore_cnt).item()
total = (t.numel() - ignore_cnt).item()
else:
count = (pred == t).sum().item()
total = t.numel()
return count, total
class SpanScorer(torch.nn.Module):
def __init__(self, num_labels: int):
super().__init__()
self.num_labels = num_labels
def forward(
self, xs: torch.Tensor, spans: Sequence[torch.Tensor]
):
raise NotImplementedError
class BaselineSpanScorer(SpanScorer):
def __init__(
self,
input_size: int,
num_labels: int,
mlp_units: Union[int, Sequence[int]] = 150,
mlp_dropout: float = 0.0,
feature="concat",
):
super().__init__(num_labels)
input_size *= 2 if feature == "concat" else 1
self.mlp = MLP(input_size, num_labels, mlp_units, F.relu, mlp_dropout)
self.feature = feature
def forward(
self, xs: torch.Tensor, spans: Sequence[torch.Tensor]
):
max_length = xs.size(1)
xs_flatten = xs.reshape(-1, xs.size(-1))
spans_flatten = torch.cat([idxs + max_length * i for i, idxs in enumerate(spans)])
features = self._compute_feature(xs_flatten, spans_flatten)
scores = self.mlp(features)
return torch.split(scores, [len(idxs) for idxs in spans])
def _compute_feature(self, xs, spans):
if self.feature == "concat":
return xs[spans.ravel()].view(len(spans), -1)
elif self.feature == "minus":
begins, ends = spans.T
return xs[ends] - xs[begins]
else:
raise NotImplementedError
class MLP(nn.Sequential):
def __init__(
self,
in_features: int,
out_features: Optional[int],
units: Optional[Union[int, Sequence[int]]] = None,
activate: Optional[Callable[[torch.Tensor], torch.Tensor]] = None,
dropout: float = 0.0,
bias: bool = True,
):
units = [units] if isinstance(units, int) else units
if not units and out_features is None:
raise ValueError("'out_features' or 'units' must be specified")
layers = []
for u in units or []:
layers.append(MLP.Layer(in_features, u, activate, dropout, bias))
in_features = u
if out_features is not None:
layers.append(MLP.Layer(in_features, out_features, None, 0.0, bias))
super().__init__(*layers)
class Layer(nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
activate: Optional[Callable[[torch.Tensor], torch.Tensor]] = None,
dropout: float = 0.0,
bias: bool = True,
):
super().__init__()
if activate is not None and not callable(activate):
raise TypeError("activate must be callable: type={}".format(type(activate)))
self.linear = nn.Linear(in_features, out_features, bias)
self.activate = activate
self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.linear(x)
if self.activate is not None:
h = self.activate(h)
return self.dropout(h)
def extra_repr(self) -> str:
return "{}, activate={}, dropout={}".format(
self.linear.extra_repr(), self.activate, self.dropout.p
)
def __repr__(self):
return "{}.{}({})".format(MLP.__name__, self._get_name(), self.extra_repr())
|