def __init__(self, config):
super(PAIR_MODULE, self).__init__()
self.H_embed = nn.Sequential(
nn.Dropout(p=config["dropout_fc"]),
GatedTrans(config["lstm_hidden_size"] * 2,
config["lstm_hidden_size"]),
)
self.Q_embed = nn.Sequential(
nn.Dropout(p=config["dropout_fc"]),
GatedTrans(config["lstm_hidden_size"] * 2,
config["lstm_hidden_size"]),
)
self.MLP = nn.Sequential(
nn.Dropout(p=config["dropout_fc"]),
nn.Linear(config["lstm_hidden_size"] * 2,
config["lstm_hidden_size"]),
nn.Dropout(p=config["dropout_fc"]),
nn.Linear(config["lstm_hidden_size"], 1))
self.att = nn.Linear(2, 1)
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight.data)
if m.bias is not None:
nn.init.constant_(m.bias.data, 0)
def __init__(self, config):
super(ATT_MODULE, self).__init__()
self.V_embed = nn.Sequential(
nn.Dropout(p=config["dropout_fc"]),
GatedTrans(config["img_feature_size"], config["lstm_hidden_size"]),
)
self.Q_embed = nn.Sequential(
nn.Dropout(p=config["dropout_fc"]),
GatedTrans(config["word_embedding_size"],
config["lstm_hidden_size"]),
)
self.att = nn.Sequential(nn.Dropout(p=config["dropout_fc"]),
nn.Linear(config["lstm_hidden_size"], 1))
self.softmax = nn.Softmax(dim=-1)
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight.data)
if m.bias is not None:
nn.init.constant_(m.bias.data, 0)