modules.py

import torch.nn.functional as F
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math


class FilterLinear(nn.Module):
    def __init__(self, in_features, out_features, filter_square_matrix, bias=True):
        '''
        filter_square_matrix : filter square matrix, whose each elements is 0 or 1.
        '''
        super(FilterLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features

        use_gpu = torch.cuda.is_available()
        self.filter_square_matrix = None
        if use_gpu:
            self.filter_square_matrix = Variable(filter_square_matrix.cuda(), requires_grad=False)
        else:
            self.filter_square_matrix = Variable(filter_square_matrix, requires_grad=False)

        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    #         print(self.weight.data)
    #         print(self.bias.data)

    def forward(self, input):
        return F.linear(input, self.filter_square_matrix.matmul(self.weight), self.bias)

    def __repr__(self):
        return self.__class__.__name__ + '(' \
               + 'in_features=' + str(self.in_features) \
               + ', out_features=' + str(self.out_features) \
               + ', bias=' + str(self.bias is not None) + ')'