def __init__(self, embed_dim, model_dim, rnn_num_layer, dropout,
column_maxlen):
super(CondsNumPredictor, self).__init__()
self.model_dim = model_dim
self.column_maxlen = column_maxlen
self.column_rnn = nn.LSTM(
input_size=embed_dim,
hidden_size=model_dim // 2,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
self.column_seq_attn = attention.LinearSeqAttn(model_dim)
self.column_to_hidden_state = nn.Linear(model_dim, 2 * model_dim)
self.column_to_cell_state = nn.Linear(model_dim, 2 * model_dim)
self.question_rnn = nn.LSTM(
input_size=embed_dim,
hidden_size=model_dim // 2,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
self.question_seq_attn = attention.LinearSeqAttn(model_dim)
self.mlp = nn.Sequential(nn.Linear(model_dim, model_dim), nn.Tanh(),
nn.Linear(model_dim, column_maxlen + 1))
def __init__(self,
embed_dim,
model_dim,
rnn_num_layer,
dropout,
column_attention=None):
super(CondsColPredictor, self).__init__()
self.column_attention = column_attention
self.question_rnn = nn.LSTM(
input_size=embed_dim,
hidden_size=model_dim // 2,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
if column_attention:
self.linear_attn = nn.Linear(model_dim, model_dim)
else:
self.seq_attn = attention.LinearSeqAttn(model_dim)
self.column_rnn = nn.LSTM(
input_size=embed_dim,
hidden_size=model_dim // 2,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
self.linear_question = nn.Linear(model_dim, model_dim)
self.linear_column = nn.Linear(model_dim, model_dim)
self.mlp = nn.Sequential(nn.ReLU(), nn.Linear(model_dim, 1))
def __init__(self, embed_dim, model_dim, rnn_num_layer, dropout,
agg_count):
super(AggPredictor, self).__init__()
self.question_rnn = nn.LSTM(
input_size=embed_dim,
hidden_size=model_dim // 2,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
self.seq_attn = attention.LinearSeqAttn(model_dim)
self.mlp = nn.Sequential(nn.Linear(model_dim, model_dim), nn.Tanh(),
nn.Linear(model_dim, agg_count))
def __init__(
self,
token_embedder,
lang_code="en",
aligned_query_embedding=False,
answer_maxlen=None,
model_dim=128,
dropout=0.3,
):
super(DrQA, self).__init__(token_embedder)
self.lang_code = lang_code
self.aligned_query_embedding = aligned_query_embedding
self.answer_maxlen = answer_maxlen
self.token_embedder = token_embedder
self.dropout = nn.Dropout(p=dropout)
context_embed_dim, query_embed_dim = token_embedder.get_embed_dim()
if self.aligned_query_embedding:
context_embed_dim += query_embed_dim
self.paragraph_rnn = nn.LSTM(
input_size=context_embed_dim,
hidden_size=model_dim,
num_layers=3,
dropout=dropout,
bidirectional=True,
batch_first=True,
)
self.query_rnn = nn.LSTM(
input_size=query_embed_dim,
hidden_size=model_dim,
num_layers=3,
dropout=dropout,
bidirectional=True,
batch_first=True,
)
self.query_att = attention.LinearSeqAttn(model_dim * 2)
self.start_attn = attention.BilinearSeqAttn(model_dim * 2,
model_dim * 2)
self.end_attn = attention.BilinearSeqAttn(model_dim * 2, model_dim * 2)
self.criterion = nn.CrossEntropyLoss()
def __init__(self, embed_dim, model_dim, rnn_num_layer, dropout,
column_maxlen, token_maxlen):
super(CondsValuePointer, self).__init__()
self.model_dim = model_dim
self.column_maxlen = column_maxlen
self.token_maxlen = token_maxlen
self.question_rnn = nn.LSTM(
input_size=embed_dim,
hidden_size=model_dim // 2,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
self.seq_attn = attention.LinearSeqAttn(model_dim)
self.column_rnn = nn.LSTM(
input_size=embed_dim,
hidden_size=model_dim // 2,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
self.decoder = nn.LSTM(
input_size=self.token_maxlen,
hidden_size=model_dim,
num_layers=rnn_num_layer,
batch_first=True,
dropout=dropout,
)
self.linear_column = nn.Linear(model_dim, model_dim)
self.linear_conds = nn.Linear(model_dim, model_dim)
self.linear_question = nn.Linear(model_dim, model_dim)
self.mlp = nn.Sequential(nn.ReLU(), nn.Linear(model_dim, 1))