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import torch
class WordEncoder(torch.nn.Module): def __init__(self, n_in, p=0.1): super(WordEncoder, self).__init__() self.n_in = n_in self.p = p self.linear = torch.nn.Linear(in_features=n_in, out_features=1) self.dp = torch.nn.Dropout(p)
def forward(self, x, indices): """ 如果indices中为padding,那么就(0, bert_out.size(1)) :param x: :param indices: :return: """ start_end_indices_to_tensor = torch.tensor(indices).to(x.device) start_indices = start_end_indices_to_tensor[:, :, 0] end_indices = start_end_indices_to_tensor[:, :, 1] mask = torch.arange(x.shape[1]).expand(x.shape[0], len(indices[0]), x.shape[1]).to(x.device) attn_mask = (mask >= start_indices.unsqueeze(2)) * (mask < end_indices.unsqueeze(2)) attn_mask = torch.log(attn_mask.to(torch.float))
attn_scores = self.linear(x).permute(0, 2, 1) attn_scores = attn_scores.expand(x.shape[0], len(indices[0]), x.shape[1]) attn_scores = attn_scores + attn_mask output = torch.softmax(attn_scores, dim=2).bmm(x) return self.dp(output)
if __name__ == '__main__': word_feature = torch.arange(60, dtype=torch.float).view(2, 5, 6) start_end_indices = [ [(0, 1), (1, 2), (2, 5)], [(0, 1), (1, 4), (0, 0)] ] w = WordEncoder(6) w(word_feature, start_end_indices)
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