| 266 | |
| 267 | class TextEncoderBiGRU(nn.Module): |
| 268 | def __init__(self, word_size, pos_size, hidden_size, device): |
| 269 | super(TextEncoderBiGRU, self).__init__() |
| 270 | self.device = device |
| 271 | |
| 272 | self.pos_emb = nn.Linear(pos_size, word_size) |
| 273 | self.input_emb = nn.Linear(word_size, hidden_size) |
| 274 | self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True, bidirectional=True) |
| 275 | # self.linear2 = nn.Linear(hidden_size, output_size) |
| 276 | |
| 277 | self.input_emb.apply(init_weight) |
| 278 | self.pos_emb.apply(init_weight) |
| 279 | # self.linear2.apply(init_weight) |
| 280 | # self.batch_size = batch_size |
| 281 | self.hidden_size = hidden_size |
| 282 | self.hidden = nn.Parameter(torch.randn((2, 1, self.hidden_size), requires_grad=True)) |
| 283 | |
| 284 | # input(batch_size, seq_len, dim) |
| 285 | def forward(self, word_embs, pos_onehot, cap_lens): |