import logging
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
[docs]class GCN(nn.Module):
def __init__(self,cfg):
super(GCN , self).__init__()
self.num_layers = cfg.num_layers
self.input_size = cfg.input_size
self.hidden_size = cfg.hidden_size
self.dropout = cfg.dropout
self.fc1 = nn.Linear(self.input_size , self.hidden_size)
self.fc = nn.Linear(self.hidden_size , self.hidden_size)
self.weight_list = nn.ModuleList()
for i in range(self.num_layers):
self.weight_list.append(nn.Linear(self.hidden_size * (i + 1),self.hidden_size))
self.dropout = nn.Dropout(self.dropout)
[docs] def forward(self , x, adj):
L = adj.sum(2).unsqueeze(2) + 1
outputs = self.fc1(x)
cache_list = [outputs]
output_list = []
for l in range(self.num_layers):
Ax = adj.bmm(outputs)
AxW = self.weight_list[l](Ax)
AxW = AxW + self.weight_list[l](outputs)
AxW = AxW / L
gAxW = F.relu(AxW)
cache_list.append(gAxW)
outputs = torch.cat(cache_list , dim=2)
output_list.append(self.dropout(gAxW))
# gcn_outputs = torch.cat(output_list, dim=2)
gcn_outputs = output_list[self.num_layers - 1]
gcn_outputs = gcn_outputs + self.fc1(x)
out = self.fc(gcn_outputs)
return out