#!/usr/bin/env python

# -*- coding: utf-8 -*-

# @Time    : 2021/8/8 16:19

# @Author  : Li Xiao

# @File    : layer.py

import torch

import math

from torch import nn

from torch.nn.parameter import Parameter

class GraphConvolution(nn.Module):

    def __init__(self, infeas, outfeas, bias=True):

        super(GraphConvolution,self).__init__()

        self.in_features = infeas

        self.out_features = outfeas

        self.weight = Parameter(torch.FloatTensor(infeas, outfeas))

        if bias:

            self.bias = Parameter(torch.FloatTensor(outfeas))

        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)

        '''

        for name, param in GraphConvolution.named_parameters(self):

            if 'weight' in name:

                #torch.nn.init.constant_(param, val=0.1)

                torch.nn.init.normal_(param, mean=0, std=0.1)

            if 'bias' in name:

                torch.nn.init.constant_(param, val=0)

        '''

    def forward(self, x, adj):

        x1 = torch.mm(x, self.weight)

        output = torch.mm(adj, x1)

        if self.bias is not None:

            return output + self.bias

        else:

            return output