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