def _trans_score(self, labels, lengths):
batch_size, seq_len = labels.shape
if self.with_start_stop_tag:
# Add START and STOP on either side of the labels
start_tensor, stop_tensor = self._get_start_stop_tensor(batch_size)
labels_ext = paddle.concat([start_tensor, labels, stop_tensor],
axis=1)
mask = paddle.cast(
sequence_mask(self._get_batch_seq_index(batch_size, seq_len),
lengths + 1), 'int32')
pad_stop = paddle.full((batch_size, seq_len + 2),
dtype='int64',
fill_value=self.stop_idx)
labels_ext = (1 - mask) * pad_stop + mask * labels_ext
else:
labels_ext = labels
start_tag_indices = labels_ext[:, :-1]
stop_tag_indices = labels_ext[:, 1:]
# Encode the indices in a flattened representation.
transition_indices = start_tag_indices * self.num_tags + stop_tag_indices
flattened_transition_indices = transition_indices.reshape([-1])
flattened_transition_params = self.transitions.reshape([-1])
scores = paddle.gather(flattened_transition_params,
flattened_transition_indices).reshape(
[batch_size, -1])
mask_scores = scores * mask[:, 1:]
# Accumulate the transition score
score = paddle.sum(mask_scores, 1)
return score
def _point_score(self, inputs, labels, lengths):
batch_size, seq_len, n_labels = inputs.shape
# Get the true label logit value
flattened_inputs = inputs.reshape([-1])
offsets = paddle.unsqueeze(
self._get_batch_index(batch_size) * seq_len * n_labels, 1)
offsets += paddle.unsqueeze(self._get_seq_index(seq_len) * n_labels, 0)
flattened_tag_indices = paddle.reshape(offsets + labels, [-1])
scores = paddle.gather(flattened_inputs,
flattened_tag_indices).reshape(
[batch_size, seq_len])
mask = paddle.cast(
sequence_mask(self._get_batch_seq_index(batch_size, seq_len),
lengths), 'float32')
mask = mask[:, :seq_len]
mask_scores = scores * mask
score = paddle.sum(mask_scores, 1)
return score