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| import torch
from torch import nn
class GraphConvolution(nn.Module):
def __init__(self, node_num, input_feature_num, output_feature_num, add_bias=True, dtype=torch.float,
batch_normal=True):
super().__init__()
# shapes
self.graph_num = node_num
self.input_feature_num = input_feature_num
self.output_feature_num = output_feature_num
self.add_bias = add_bias
self.batch_normal = batch_normal
# params
self.weight = nn.Parameter(torch.empty(self.output_feature_num, input_feature_num, dtype=dtype))
self.bias = nn.Parameter(torch.empty(self.output_feature_num, self.graph_num, dtype=dtype))
if batch_normal:
self.norm = nn.InstanceNorm1d(node_num)
def set_trainable(self, train=True):
for param in self.parameters():
param.requires_grad = train
def forward(self, inp: torch.Tensor):
"""
@param inp : adjacent: (batch, graph_num, graph_num) cat node_feature: (batch, graph_num, in_feature_num) -> (batch, graph_num, graph_num + in_feature_num)
@return:
"""
b, c, n = inp.shape
adjacent, node_feature = inp[:, 0:n, :], inp[:, n:, :]
x = torch.matmul(self.weight, node_feature)
x = torch.matmul(x, adjacent)
if self.add_bias:
x = x + self.bias
if self.batch_normal:
x = self.norm(x)
return torch.cat((adjacent, x), dim=1)
|