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 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)
_, (h, _) = self.lstm(emb, char_mask, self.pad_index)
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 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