def decode(self, input_word, input_char, input_pos, mask=None, leading_symbolic=0):
"""
Args:
input_word: Tensor
the word input tensor with shape = [batch, length]
input_char: Tensor
the character input tensor with shape = [batch, length, char_length]
input_pos: Tensor
the pos input tensor with shape = [batch, length]
mask: Tensor or None
the mask tensor with shape = [batch, length]
length: Tensor or None
the length tensor with shape = [batch]
hx: Tensor or None
the initial states of RNN
leading_symbolic: int
number of symbolic labels leading in type alphabets (set it to 0 if you are not sure)
Returns: (Tensor, Tensor)
predicted heads and types.
"""
# out_arc shape [batch, length_h, length_c]
energy, out_type = self(input_word, input_char, input_pos, mask=mask)
# compute lengths
length = mask.sum(dim=1).long()
heads, _ = parser.decode_MST(energy.cpu().numpy(), length.cpu().numpy(), leading_symbolic=leading_symbolic, labeled=False)
types = self._decode_types(out_type, torch.from_numpy(heads).type_as(length), leading_symbolic)
return heads, types.cpu().numpy()
def decode(self,
input_word,
input_char,
input_pos,
mask=None,
leading_symbolic=0):
"""
Args:
input_word: Tensor
the word input tensor with shape = [batch, length]
input_char: Tensor
the character input tensor with shape = [batch, length, char_length]
input_pos: Tensor
the pos input tensor with shape = [batch, length]
mask: Tensor or None
the mask tensor with shape = [batch, length]
length: Tensor or None
the length tensor with shape = [batch]
hx: Tensor or None
the initial states of RNN
leading_symbolic: int
number of symbolic labels leading in type alphabets (set it to 0 if you are not sure)
Returns: (Tensor, Tensor)
predicted heads and types.
"""
# out_arc shape [batch, length_h, length_c]
out_arc, out_type = self(input_word, input_char, input_pos, mask=mask)
# out_type shape [batch, length, type_space]
type_h, type_c = out_type
batch, max_len, type_space = type_h.size()
type_h = type_h.unsqueeze(2).expand(batch, max_len, max_len,
type_space).contiguous()
type_c = type_c.unsqueeze(1).expand(batch, max_len, max_len,
type_space).contiguous()
# compute output for type [batch, length_h, length_c, num_labels]
out_type = self.bilinear(type_h, type_c)
if mask is not None:
minus_mask = mask.eq(0).unsqueeze(2)
out_arc.masked_fill_(minus_mask, float('-inf'))
# loss_arc shape [batch, length_h, length_c]
loss_arc = F.log_softmax(out_arc, dim=1)
# loss_type shape [batch, length_h, length_c, num_labels]
loss_type = F.log_softmax(out_type, dim=3).permute(0, 3, 1, 2)
# [batch, num_labels, length_h, length_c]
energy = loss_arc.unsqueeze(1) + loss_type
# compute lengths
length = mask.sum(dim=1).long().cpu().numpy()
return parser.decode_MST(energy.cpu().numpy(),
length,
leading_symbolic=leading_symbolic,
labeled=True)
def decode_mst(self, input_word, input_char, input_pos, mask=None, length=None, hx=None, leading_symbolic=0):
'''
Args:
input_word: Tensor
the word input tensor with shape = [batch, length]
input_char: Tensor
the character input tensor with shape = [batch, length, char_length]
input_pos: Tensor
the pos input tensor with shape = [batch, length]
mask: Tensor or None
the mask tensor with shape = [batch, length]
length: Tensor or None
the length tensor with shape = [batch]
hx: Tensor or None
the initial states of RNN
leading_symbolic: int
number of symbolic labels leading in type alphabets (set it to 0 if you are not sure)
Returns: (Tensor, Tensor)
predicted heads and types.
'''
# out_arc shape [batch, length, length]
out_arc, out_type, mask, length = self.forward(input_word, input_char, input_pos,
mask=mask, length=length, hx=hx)
# out_type shape [batch, length, type_space]
type_h, type_c = out_type
batch, max_len, type_space = type_h.size()
# compute lengths
if length is None:
if mask is None:
length = [max_len for _ in range(batch)]
else:
length = mask.data.sum(dim=1).long().cpu().numpy()
type_h = type_h.unsqueeze(2).expand(batch, max_len, max_len, type_space).contiguous()
type_c = type_c.unsqueeze(1).expand(batch, max_len, max_len, type_space).contiguous()
# compute output for type [batch, length, length, num_labels]
out_type = self.bilinear(type_h, type_c)
# mask invalid position to -inf for log_softmax
if mask is not None:
minus_inf = -1e8
minus_mask = (1 - mask) * minus_inf
out_arc = out_arc + minus_mask.unsqueeze(2) + minus_mask.unsqueeze(1)
# loss_arc shape [batch, length, length]
loss_arc = F.log_softmax(out_arc, dim=1)
# loss_type shape [batch, length, length, num_labels]
loss_type = F.log_softmax(out_type, dim=3).permute(0, 3, 1, 2)
# [batch, num_labels, length, length]
energy = torch.exp(loss_arc.unsqueeze(1) + loss_type)
return parser.decode_MST(energy.data.cpu().numpy(), length, leading_symbolic=leading_symbolic, labeled=True)
