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import math |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from torch.nn.parameter import Parameter |
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class BiLinear(nn.Module): |
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''' |
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Bi-linear layer |
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''' |
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def __init__(self, left_features, right_features, out_features, bias=True): |
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''' |
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Args: |
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left_features: size of left input |
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right_features: size of right input |
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out_features: size of output |
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bias: If set to False, the layer will not learn an additive bias. |
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Default: True |
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''' |
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super(BiLinear, self).__init__() |
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self.left_features = left_features |
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self.right_features = right_features |
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self.out_features = out_features |
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self.U = Parameter(torch.Tensor(self.out_features, self.left_features, self.right_features)) |
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self.W_l = Parameter(torch.Tensor(self.out_features, self.left_features)) |
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self.W_r = Parameter(torch.Tensor(self.out_features, self.left_features)) |
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if bias: |
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self.bias = Parameter(torch.Tensor(out_features)) |
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else: |
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self.register_parameter('bias', None) |
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self.reset_parameters() |
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def reset_parameters(self): |
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nn.init.xavier_uniform_(self.W_l) |
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nn.init.xavier_uniform_(self.W_r) |
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nn.init.constant_(self.bias, 0.) |
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nn.init.xavier_uniform_(self.U) |
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def forward(self, input_left, input_right): |
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''' |
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Args: |
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input_left: Tensor |
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the left input tensor with shape = [batch1, batch2, ..., left_features] |
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input_right: Tensor |
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the right input tensor with shape = [batch1, batch2, ..., right_features] |
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Returns: |
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''' |
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left_size = input_left.size() |
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right_size = input_right.size() |
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assert left_size[:-1] == right_size[:-1], \ |
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"batch size of left and right inputs mis-match: (%s, %s)" % (left_size[:-1], right_size[:-1]) |
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batch_size = int(np.prod(left_size[:-1])) |
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input_left = input_left.view(batch_size, self.left_features) |
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input_right = input_right.view(batch_size, self.right_features) |
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output = F.bilinear(input_left, input_right, self.U, self.bias) |
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output = output + F.linear(input_left, self.W_l, None) + F.linear(input_right, self.W_r, None) |
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return output.view(left_size[:-1] + (self.out_features, )) |
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def __repr__(self): |
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return self.__class__.__name__ + ' (' \ |
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+ 'in1_features=' + str(self.left_features) \ |
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+ ', in2_features=' + str(self.right_features) \ |
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+ ', out_features=' + str(self.out_features) + ')' |
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