def sdp(self, hidden: dict, graph=True):
# 语义依存
sdp_arc, sdp_label, _ = self.model.sdp_decoder(hidden['word_cls_input'], hidden['word_length'])
sdp_arc = torch.sigmoid_(sdp_arc)
if graph:
# 语义依存图
sdp_arc.transpose_(-1, -2)
sdp_root_mask = sdp_arc[:, 0].argmax(dim=-1).unsqueeze_(-1).expand_as(sdp_arc[:, 0])
sdp_arc[:, 0] = 0
sdp_arc[:, 0].scatter_(dim=-1, index=sdp_root_mask, value=1)
sdp_arc_T = sdp_arc.transpose(-1, -2)
sdp_arc_fix = sdp_arc_T.argmax(dim=-1).unsqueeze_(-1).expand_as(sdp_arc)
sdp_arc = ((sdp_arc_T > 0.5) & (sdp_arc_T > sdp_arc)). \
scatter_(dim=-1, index=sdp_arc_fix, value=True)
else:
# 语义依存树
sdp_arc_fix = eisner(sdp_arc, hidden['word_cls_mask']).unsqueeze_(-1).expand_as(sdp_arc)
sdp_arc = torch.zeros_like(sdp_arc, dtype=torch.bool).scatter_(dim=-1, index=sdp_arc_fix, value=True)
sdp_label = torch.argmax(sdp_label, dim=-1)
word_cls_mask = hidden['word_cls_mask']
word_cls_mask = word_cls_mask.unsqueeze(-1).expand(-1, -1, word_cls_mask.size(1))
sdp_arc = sdp_arc & word_cls_mask
sdp_label = self._get_graph_entities(sdp_arc, sdp_label, self.sdp_vocab)
return sdp_label
def dep(self, hidden: dict, fast=False):
# 依存句法树
dep_arc, dep_label, word_length = self.model.dep_decoder(
hidden['word_cls_input'], hidden['word_length'])
if fast:
dep_arc_fix = dep_arc.argmax(
dim=-1).unsqueeze_(-1).expand_as(dep_arc)
else:
dep_arc_fix = eisner(
dep_arc,
hidden['word_cls_mask']).unsqueeze_(-1).expand_as(dep_arc)
dep_arc = torch.zeros_like(dep_arc, dtype=torch.bool).scatter_(
dim=-1, index=dep_arc_fix, value=True)
dep_label[:, :, :, self.dep_fix:] = float('-inf')
dep_label = torch.argmax(dep_label, dim=-1)
word_cls_mask = hidden['word_cls_mask']
word_cls_mask = word_cls_mask.unsqueeze(-1).expand(
-1, -1, word_cls_mask.size(1))
dep_arc = dep_arc & word_cls_mask
dep_label = self._get_graph_entities(dep_arc, dep_label,
self.dep_vocab)
return dep_label
def dep(self, hidden: dict, fast=False):
"""
依存句法树
Args:
hidden: 分词时所得到的中间表示
fast: 启用 fast 模式时，减少对结果的约束，速度更快，相应的精度会降低
Returns:
依存句法树结果
"""
dep_arc, dep_label, word_length = self.model.dep_decoder(
hidden['word_cls_input'], hidden['word_length'])
if fast:
dep_arc_fix = dep_arc.argmax(
dim=-1).unsqueeze_(-1).expand_as(dep_arc)
else:
dep_arc_fix = eisner(
dep_arc,
hidden['word_cls_mask']).unsqueeze_(-1).expand_as(dep_arc)
dep_arc = torch.zeros_like(dep_arc, dtype=torch.bool).scatter_(
-1, dep_arc_fix, True)
dep_label = torch.argmax(dep_label, dim=-1)
word_cls_mask = hidden['word_cls_mask']
word_cls_mask = word_cls_mask.unsqueeze(-1).expand(
-1, -1, word_cls_mask.size(1))
dep_arc = dep_arc & word_cls_mask
dep_label = get_graph_entities(dep_arc, dep_label, self.dep_vocab)
return dep_label
def step(self, y_pred: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
y: Tuple[torch.Tensor, torch.Tensor]):
arc_pred, label_pred, seq_len = y_pred
mask = length_to_mask(seq_len + 1)
mask[:, 0] = False
if self._eisner:
from ltp.utils import eisner
arc_pred = eisner(arc_pred, mask)
else:
arc_pred = torch.argmax(arc_pred, dim=-1)
label_pred = torch.argmax(label_pred, dim=-1)
arc_real, label_real = y
label_pred = label_pred.gather(-1, arc_pred.unsqueeze(-1)).squeeze(-1)
mask = mask.narrow(-1, 1, mask.size(1) - 1)
arc_pred = arc_pred.narrow(-1, 1, arc_pred.size(1) - 1)
label_pred = label_pred.narrow(-1, 1, label_pred.size(1) - 1)
head_true = (arc_pred == arc_real)[mask]
label_true = (label_pred == label_real)[mask]
self._head_true += torch.sum(head_true).item()
self._label_true += torch.sum(label_true).item()
self._union_true += torch.sum(label_true[head_true]).item()
self._all += torch.sum(mask).item()
def sdp(self, hidden: dict, graph=True):
"""
语义依存图（树）
Args:
hidden: 分词时所得到的中间表示
graph: 选择是语义依存图还是语义依存树结果
Returns:
语义依存图（树）结果
"""
sdp_arc, sdp_label, _ = self.model.sdp_decoder(
hidden['word_cls_input'], hidden['word_length'])
sdp_arc = torch.sigmoid_(sdp_arc)
# 避免自指
eye = torch.arange(0, sdp_arc.size(1), device=sdp_arc.device).view(
1, 1, -1).expand(sdp_arc.size(0), -1, -1)
sdp_arc.scatter_(1, eye, 0)
if graph:
# 语义依存图
sdp_arc.transpose_(-1, -2)
sdp_root_mask = sdp_arc[:,
0].argmax(dim=-1).unsqueeze_(-1).expand_as(
sdp_arc[:, 0])
sdp_arc[:, 0] = 0
sdp_arc[:, 0].scatter_(dim=-1, index=sdp_root_mask, value=1)
sdp_arc_T = sdp_arc.transpose(-1, -2)
sdp_arc_fix = sdp_arc_T.argmax(
dim=-1).unsqueeze_(-1).expand_as(sdp_arc)
sdp_arc = ((sdp_arc_T > 0.5) & (sdp_arc_T > sdp_arc)).scatter_(
-1, sdp_arc_fix, True)
else:
# 语义依存树
sdp_arc_fix = eisner(
sdp_arc,
hidden['word_cls_mask']).unsqueeze_(-1).expand_as(sdp_arc)
sdp_arc = torch.zeros_like(sdp_arc, dtype=torch.bool).scatter_(
-1, sdp_arc_fix, True)
sdp_label = torch.argmax(sdp_label, dim=-1)
word_cls_mask = hidden['word_cls_mask']
word_cls_mask = word_cls_mask.unsqueeze(-1).expand(
-1, -1, word_cls_mask.size(1))
word_cls_mask = word_cls_mask & word_cls_mask.transpose(-1, -2)
for si, length in enumerate(hidden['word_length']):
word_cls_mask[si, :length, 0] = True
sdp_arc = sdp_arc & word_cls_mask
sdp_label = get_graph_entities(sdp_arc, sdp_label, self.sdp_vocab)
return sdp_label
