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import torch.nn as nn |
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from model.module.bilinear import Bilinear |
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class Biaffine(nn.Module): |
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def __init__(self, input_dim, output_dim, bias=True, bias_init=None): |
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super(Biaffine, self).__init__() |
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self.linear_1 = nn.Linear(input_dim, output_dim, bias=False) |
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self.linear_2 = nn.Linear(input_dim, output_dim, bias=False) |
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self.bilinear = Bilinear(input_dim, input_dim, output_dim, bias=bias) |
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if bias_init is not None: |
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self.bilinear.bias.data = bias_init |
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def forward(self, x, y): |
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return self.bilinear(x, y) + self.linear_1(x).unsqueeze(2) + self.linear_2(y).unsqueeze(1) |
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