| import torch | |
| import torch.nn as nn | |
| from torch.nn.utils.rnn import pack_padded_sequence | |
| class TextEncoderBiGRUCo(nn.Module): | |
| def __init__(self, word_size: int, pos_size: int, hidden_size: int, output_size: int) -> None: | |
| super(TextEncoderBiGRUCo, self).__init__() | |
| self.pos_emb = nn.Linear(pos_size, word_size) | |
| self.input_emb = nn.Linear(word_size, hidden_size) | |
| self.gru = nn.GRU( | |
| hidden_size, hidden_size, batch_first=True, bidirectional=True | |
| ) | |
| self.output_net = nn.Sequential( | |
| nn.Linear(hidden_size * 2, hidden_size), | |
| nn.LayerNorm(hidden_size), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| nn.Linear(hidden_size, output_size), | |
| ) | |
| self.hidden_size = hidden_size | |
| self.hidden = nn.Parameter( | |
| torch.randn((2, 1, self.hidden_size), requires_grad=True) | |
| ) | |
| def forward(self, word_embs: torch.Tensor, pos_onehot: torch.Tensor, | |
| cap_lens: torch.Tensor) -> torch.Tensor: | |
| num_samples = word_embs.shape[0] | |
| pos_embs = self.pos_emb(pos_onehot) | |
| inputs = word_embs + pos_embs | |
| input_embs = self.input_emb(inputs) | |
| hidden = self.hidden.repeat(1, num_samples, 1) | |
| cap_lens = cap_lens.data.tolist() | |
| emb = pack_padded_sequence(input_embs, cap_lens, batch_first=True) | |
| gru_seq, gru_last = self.gru(emb, hidden) | |
| gru_last = torch.cat([gru_last[0], gru_last[1]], dim=-1) | |
| return self.output_net(gru_last) | |