def __init__(self,
hidden_size,
output_size=1,
n_layers=1,
use_cuda=True,
attn_type='dot',
context_type=None):
super(Encoder_Decoder, self).__init__()
self.n_layers = n_layers
self.hidden_size = hidden_size
self.use_cuda = use_cuda
self.output_size = output_size
self.bidirectional = True
if self.bidirectional:
self.num_directions = 2
else:
self.num_directions = 1
# encoder
self.appear_linear = nn.Linear(1024, hidden_size)
self.feature_linear = nn.Linear(hidden_size + 96, hidden_size)
self.encoder_rnn = nn.ModuleList([
nn.GRU(hidden_size,
hidden_size,
self.n_layers,
bidirectional=self.bidirectional) for i in range(80)
])
# decoder_rnn
self.decoder_rnn = nn.ModuleList([
nn.GRU(hidden_size,
hidden_size,
self.n_layers,
bidirectional=self.bidirectional) for i in range(80)
])
self.out = nn.Linear(2 * hidden_size, 1)
self.sigmoid = nn.Sigmoid()
# attention
self.atten = GlobalAttention(hidden_size * 2, attn_type=attn_type)
self.selfatten = GlobalAttention(hidden_size, attn_type=attn_type)
# context
self.context_type = context_type
if self.context_type is not None:
self.context_gate = context_gate_factory(self.context_type,
hidden_size,
hidden_size * 2,
hidden_size * 2,
hidden_size * 2)
def __init__(self, hidden_size, output_size=1, n_layers=1, use_cuda=True, attn_type='dot'):
super(Encoder_Decoder, self).__init__()
self.n_layers = n_layers
self.hidden_size = hidden_size
self.use_cuda = use_cuda
self.output_size = output_size
# encoder
self.appear_linear = nn.Linear(1024, hidden_size)
self.feature_linear = nn.Linear(hidden_size+96, hidden_size)
self.encoder_rnn = nn.ModuleList([nn.GRU(hidden_size, hidden_size, bidirectional=True) for i in range(80)])
# decoder_rnn
self.decoder_rnn = nn.ModuleList([nn.GRU(hidden_size, hidden_size, bidirectional=True) for i in range(80)])
self.out = nn.Linear(2*hidden_size, 1)
self.sigmoid = nn.Sigmoid()
# attention
self.atten = GlobalAttention(hidden_size*2, attn_type=attn_type)