def forward(self, x):
"""Forward network"""
mask = layers.reduce_any(x != self.pad_index, -1)
lens = nn.reduce_sum(mask, -1)
masked_x = nn.masked_select(x, mask)
char_mask = masked_x != self.pad_index
emb = self.embed(masked_x)
word_lens = nn.reduce_sum(char_mask, -1)
_, (h, _) = self.lstm(emb, sequence_length=word_lens)
h = layers.concat(layers.unstack(h), axis=-1)
feat_embed = nn.pad_sequence_paddle(
layers.split(h, lens.numpy().tolist(), dim=0), self.pad_index)
return feat_embed
def epoch_predict(env, args, model, loader):
"""Predict in one epoch"""
model.eval()
arcs, rels, probs = [], [], []
for words, feats in loader():
# ignore the first token of each sentence
tmp_words = layers.pad(words[:, 1:],
paddings=[0, 0, 1, 0],
pad_value=args.pad_index)
mask = tmp_words != args.pad_index
lens = nn.reduce_sum(mask, -1)
s_arc, s_rel = model(words, feats)
arc_preds, rel_preds = decode(args, s_arc, s_rel, mask)
arcs.extend(
layers.split(nn.masked_select(arc_preds, mask),
lens.numpy().tolist()))
rels.extend(
layers.split(nn.masked_select(rel_preds, mask),
lens.numpy().tolist()))
if args.prob:
arc_probs = nn.index_sample(layers.softmax(s_arc, -1),
layers.unsqueeze(arc_preds, -1))
probs.extend(
layers.split(
nn.masked_select(layers.squeeze(arc_probs, axes=[-1]),
mask),
lens.numpy().tolist()))
arcs = [seq.numpy().tolist() for seq in arcs]
rels = [env.REL.vocab[seq.numpy().tolist()] for seq in rels]
probs = [[round(p, 3) for p in seq.numpy().tolist()] for seq in probs]
return arcs, rels, probs
def forward(self, x):
"""Forward network"""
mask = layers.reduce_any(x != self.pad_index, -1)
lens = nn.reduce_sum(mask, -1)
masked_x = nn.masked_select(x, mask)
h, _ = self.transformer(masked_x)
feat_embed = nn.pad_sequence_paddle(
layers.split(h, lens.numpy().tolist(), dim=0), self.pad_index)
return feat_embed
def flat_words(self, words):
pad_index = self.args.pad_index
lens = nn.reduce_sum(words != pad_index, dim=-1)
position = layers.cumsum(lens + layers.cast((lens == 0), "int32"),
axis=1) - 1
flat_words = nn.masked_select(words, words != pad_index)
flat_words = nn.pad_sequence_paddle(
layers.split(flat_words,
layers.reduce_sum(lens, -1).numpy().tolist(),
pad_index))
max_len = flat_words.shape[1]
position = nn.mask_fill(position, position >= max_len, max_len - 1)
return flat_words, position
def epoch_predict(env, args, model, loader):
"""Predict in one epoch"""
connections, deprels, probabilities = [], [], []
pad_index = args.pad_index
bos_index = args.bos_index
eos_index = args.eos_index
for batch, inputs in enumerate(loader(), start=1):
if args.encoding_model.startswith("ernie"):
words = inputs[0]
connection_prob, deprel_prob, words = model(words)
else:
words, feats = inputs
connection_prob, deprel_prob, words = model(words, feats)
mask = layers.logical_and(
layers.logical_and(words != pad_index, words != bos_index),
words != eos_index,
)
lens = nn.reduce_sum(mask, -1)
connection_predicts, deprel_predicts = decode(args, connection_prob,
deprel_prob, mask)
connections.extend(
layers.split(nn.masked_select(connection_predicts, mask),
lens.numpy().tolist()))
deprels.extend(
layers.split(nn.masked_select(deprel_predicts, mask),
lens.numpy().tolist()))
if args.prob:
arc_probs = nn.index_sample(
layers.softmax(connection_prob, -1),
layers.unsqueeze(connection_predicts, -1))
probabilities.extend(
layers.split(
nn.masked_select(layers.squeeze(arc_probs, axes=[-1]),
mask),
lens.numpy().tolist(),
))
connections = [seq.numpy().tolist() for seq in connections]
deprels = [env.REL.vocab[seq.numpy().tolist()] for seq in deprels]
probabilities = [[round(p, 3) for p in seq.numpy().tolist()]
for seq in probabilities]
return connections, deprels, probabilities
def forward(self, x, seq_mask):
"""Forward network"""
seq_lens = nn.reduce_sum(seq_mask, -1)
y, _ = self.lstm(x, sequence_length=seq_lens)
return y
