def __init__(self,
n_src_vocab=hp.vocab_size,
n_src_note=hp.note_size,
len_max_seq=hp.max_sep_len,
d_word_vec=hp.word_vec_dim,
n_layers=hp.encoder_n_layer,
n_head=hp.encoder_head,
d_k=64,
d_v=64,
d_model=hp.word_vec_dim,
d_inner=hp.encoder_conv1d_filter_size,
dropout=hp.dropout):
super(Encoder, self).__init__()
n_position = len_max_seq + 1
self.src_word_emb = nn.Embedding(n_src_vocab + 1,
d_word_vec,
padding_idx=Constants.PAD)
self.src_note_emb = nn.Embedding(n_src_note + 1,
d_word_vec,
padding_idx=Constants.PAD)
self.position_enc = nn.Embedding.from_pretrained(
get_sinusoid_encoding_table(n_position, d_word_vec, padding_idx=0),
freeze=True)
self.layer_stack = nn.ModuleList([
FFTBlock(d_model, d_inner, n_head, d_k, d_v, dropout=dropout),
FFTBlock(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
])
def __init__(self,
len_max_seq=hp.max_sep_len,
d_word_vec=hp.word_vec_dim,
n_layers=hp.decoder_n_layer,
n_head=hp.decoder_head,
d_k=64,
d_v=64,
d_model=hp.word_vec_dim,
d_inner=hp.decoder_conv1d_filter_size,
dropout=hp.dropout):
super(Decoder, self).__init__()
n_position = len_max_seq + 1
self.position_enc = nn.Embedding.from_pretrained(
get_sinusoid_encoding_table(n_position, d_word_vec, padding_idx=0),
freeze=True)
self.layer_stack = nn.ModuleList([
FFTBlock(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
for _ in range(n_layers)
])