def __init__(self, opt):
super(MixerModel, self).__init__()
self.vocab_size = opt.vocab_size
self.input_encoding_size = opt.input_encoding_size
self.rnn_type = opt.rnn_type
self.rnn_size = opt.rnn_size
self.num_layers = opt.num_layers
self.drop_prob_lm = opt.drop_prob_lm
self.seq_length = opt.seq_length
self.fc_feat_size = opt.fc_feat_size
self.att_feat_size = opt.att_feat_size
self.att_size = opt.att_size
self.batch_size = 80
# LSTM
self.core = LSTM.LSTM_DOUBLE_ATT_TOP(self.input_encoding_size,
self.vocab_size + 1,
self.rnn_size,
self.att_size,
dropout=self.drop_prob_lm)
# self.vocab_size + 1 -> self.input_encoding_size
self.embed = nn.Embedding(self.vocab_size + 1,
self.input_encoding_size)
# (batch_size * fc_feat_size) -> (batch_size * input_encoding_size)
self.img_embed = nn.Linear(self.fc_feat_size, self.input_encoding_size)
self.att_embed = nn.Linear(self.att_feat_size,
self.input_encoding_size)
def __init__(self, opt):
super(BiShowAttenTellModel, self).__init__()
self.vocab_size = opt.vocab_size
self.input_encoding_size = opt.input_encoding_size
self.rnn_type = opt.rnn_type
self.rnn_size = opt.rnn_size
self.num_layers = opt.num_layers
self.drop_prob_lm = opt.drop_prob_lm
self.seq_length = opt.seq_length
self.fc_feat_size = opt.fc_feat_size
self.att_feat_size = opt.att_feat_size
self.att_size = opt.att_size
self.output_size = self.vocab_size + 1
# LSTM
# self.core = nn.LSTM(self.input_encoding_size, self.rnn_size, self.num_layers, bias=False, dropout=self.drop_prob_lm)
if self.rnn_type == "LSTM_SOFT_ATT":
self.core = LSTM.LSTM_SOFT_ATT_TOP(self.input_encoding_size, self.output_size, self.rnn_size, self.att_size, dropout=self.drop_prob_lm)
self.core1 = LSTM.LSTM_SOFT_ATT_TOP(self.input_encoding_size, self.output_size, self.rnn_size, self.att_size, dropout=self.drop_prob_lm)
elif self.rnn_type == "LSTM_DOUBLE_ATT":
self.core = LSTM.LSTM_DOUBLE_ATT_TOP(self.input_encoding_size, self.output_size, self.rnn_size, self.att_size, dropout=self.drop_prob_lm)
self.core1 = LSTM.LSTM_DOUBLE_ATT_TOP(self.input_encoding_size, self.output_size, self.rnn_size, self.att_size, dropout=self.drop_prob_lm)
else:
raise Exception("rnn type not supported: {}".format(self.rnn_type))
# self.vocab_size + 1 -> self.input_encoding_size
self.embed = nn.Embedding(self.vocab_size + 1, self.input_encoding_size)
self.img_embed = nn.Linear(self.fc_feat_size, self.rnn_size)
self.att_embed = nn.Linear(self.att_feat_size, self.rnn_size)
self.proj = nn.Linear(self.rnn_size, self.output_size)
# self.relu = nn.RReLU(inplace=True)
self.relu = nn.PReLU()
self.init_weight()