def __init__(self, opt, dicts):
super(HierModel, self).__init__()
self.set_embeddings(opt, dicts)
mem = opt.mem.split('_')
self.sen_encoder = nn.LSTM(opt.word_vec_size,
opt.word_vec_size // 2,
num_layers=2,
dropout=opt.dropout,
bidirectional=1)
if mem[0] == 'lstm':
opt.rnn_size = self.embed_in.weight.size(1)
self.diag_encoder = lstms.LSTMseq(opt, dicts, 'encode')
elif mem[0] == 'dnc':
self.diag_encoder = dnc.DNC(opt, 'diag_encode')
if mem[1] == 'lstm':
opt.word_vec_size = self.embed_out.weight.size(1)
self.decoder = lstms.LSTMseq(opt, dicts, 'decode')
elif mem[1] == 'dnc':
self.decoder = dnc.DNC(opt, 'decode')
self.merge_hidden = opt.merge_hidden
if self.merge_hidden:
self.merge_h = nn.Linear(2 * opt.word_vec_size, opt.word_vec_size)
self.merge_c = nn.Linear(2 * opt.word_vec_size, opt.word_vec_size)
self.forward = eval('self.hier_' + opt.mem)
self.generate = False
def __init__(self, opt, dicts):
super(HierModel, self).__init__()
self.set_embeddings(opt, dicts)
mem = opt.mem.split('_')
def bi_lstm(l):
return nn.LSTM(opt.word_vec_size,
opt.word_vec_size // 2,
num_layers=l,
dropout=opt.dropout,
bidirectional=1)
# nn hierarchical models
if opt.mem == 'dnc_dnc':
opt.dropout = .6
opt.attn = 0
self.diag_encoder = dnc.DNC(opt, 'encode')
self.decoder = dnc.DNC(opt, 'decode')
elif opt.mem == 'baseline':
self.diag_encoder = bi_lstm(2)
self.decoder = lstms.LSTMseq(opt, dicts, 'decode')
elif opt.mem == 'reasoning_nse':
self.utt_encoder = bi_lstm(1)
self.utt_decoder = lstms.LSTMseq(opt, dicts, 'init_decode')
self.context_mem = bi_lstm(2)
self.decoder = reasoning_nse.Tweak(opt)
# hierarchical models
else:
mem = opt.mem.split('_')
if mem[0] == 'lstm':
self.utt_encoder = bi_lstm(2)
elif mem[0] == 'dnc':
opt.dropout = .6
self.utt_encoder = dnc.DNC(opt, 'encode')
if mem[1] == 'lstm':
self.diag_encoder = bi_lstm(2)
self.decoder = lstms.LSTMseq(opt, dicts, 'decode')
elif mem[1] == 'dnc':
opt.dropout = .6
self.diag_encoder = dnc.DNC(opt, 'encode')
self.decoder = dnc.DNC(opt, 'decode')
self.forward = eval('self.' + opt.mem)
self.generate = False
def __init__(self, opt, dicts):
super(MemModel, self).__init__()
self.set_embeddings(opt, dicts)
mem = opt.mem.split('_')
if mem[0] == 'lstm':
opt.rnn_size = self.embed_in.weight.size(1)
self.encoder = lstms.LSTMseq(opt, dicts, 'encode')
elif mem[0] == 'dnc':
self.encoder = dnc.DNC(opt, 'encode')
if mem[1] == 'lstm':
opt.word_vec_size = self.embed_out.weight.size(1)
self.decoder = lstms.LSTMseq(opt, dicts, 'decode')
elif mem[1] == 'dnc':
self.decoder = dnc.DNC(opt, 'decode')
self.forward = eval('self.' + opt.mem)
self.generate = False
def __init__(self, opt, dicts):
super(HierDAModel, self).__init__()
self.set_embeddings(opt, dicts)
mem = opt.mem.split('_')
def bi_lstm(l):
return nn.LSTM(opt.word_vec_size,
opt.word_vec_size // 2,
num_layers=l,
dropout=opt.dropout,
bidirectional=1)
if opt.mem == 'DAreasoning_nse':
self.utt_encoder = bi_lstm(1)
self.utt_decoder = lstms.LSTMseq(opt, dicts, 'init_decode')
self.context_mem = bi_lstm(2)
self.decoder = reasoning_nse.Tweak(opt)
if mem[1] == 'baseline':
self.merge = nn.Sequential(
nn.Linear(2 * opt.word_vec_size, opt.word_vec_size), nn.Tanh())
self.diag_encoder = bi_lstm(2)
self.decoder = lstms.LSTMseq(opt, dicts, 'decode')
else:
if mem[0] == 'lstm':
self.utt_encoder = bi_lstm(2)
self.merge = nn.Sequential(
nn.Linear(2 * opt.word_vec_size, opt.word_vec_size),
nn.Tanh())
if mem[1] == 'hierda':
self.diag_encoder = bi_lstm(2)
self.decoder = lstms.LSTMseq(opt, dicts, 'decode')
self.forward = eval('self.' + opt.mem)
self.generate = False
def __init__(self, opt, dicts):
super(KeyContModel, self).__init__()
self.set_embeddings(opt, dicts)
mem = opt.mem.split('_')
self.act_embedding = nn.Embedding(opt.act_vec_size,
opt.word_vec_size,
padding_idx=Constants.PAD)
def bi_lstm(l):
return nn.LSTM(opt.word_vec_size,
opt.word_vec_size // 2,
num_layers=l,
dropout=opt.dropout,
bidirectional=1)
self.sen_encoder = bi_lstm(2)
self.sen_decoder = lstms.LSTMseq(opt, dicts, 'decode')
if mem[0] == 'lstm':
self.context_encoder = nn.LSTM(opt.word_vec_size,
opt.word_vec_size,
num_layers=1,
dropout=opt.dropout,
bidirectional=0)
if mem[0] == 'dnc':
opt.dropout = .6
self.context_encoder = dnc.DNC(opt, 'encode')
if mem[1] == 'lstm':
self.context_attention = memories.attention.GlobalAttention(
opt.word_vec_size)
elif mem[1] == 'dnc':
opt.dropout = .6
opt.attn = 0
self.context_attention = dnc.DNC(opt, 'act_decode')
self.forward = eval('self.' + opt.mem)
self.generate = False