dmlc--dgl
HQ
81 行
2.7 KiB
Python
81 行
2.7 KiB
Python
import copy
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import itertools
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import dgl
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import dgl.function as fn
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import networkx as nx
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import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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class GNNModule(nn.Module):
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def __init__(self, in_feats, out_feats, radius):
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super().__init__()
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self.out_feats = out_feats
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self.radius = radius
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new_linear = lambda: nn.Linear(in_feats, out_feats)
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new_linear_list = lambda: nn.ModuleList([new_linear() for i in range(radius)])
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self.theta_x, self.theta_deg, self.theta_y = \
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new_linear(), new_linear(), new_linear()
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self.theta_list = new_linear_list()
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self.gamma_y, self.gamma_deg, self.gamma_x = \
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new_linear(), new_linear(), new_linear()
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self.gamma_list = new_linear_list()
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self.bn_x = nn.BatchNorm1d(out_feats)
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self.bn_y = nn.BatchNorm1d(out_feats)
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def aggregate(self, g, z):
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z_list = []
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g.set_n_repr({'z' : z})
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g.update_all(fn.copy_src(src='z', out='m'), fn.sum(msg='m', out='z'))
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z_list.append(g.get_n_repr()['z'])
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for i in range(self.radius - 1):
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for j in range(2 ** i):
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g.update_all(fn.copy_src(src='z', out='m'), fn.sum(msg='m', out='z'))
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z_list.append(g.get_n_repr()['z'])
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return z_list
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def forward(self, g, lg, x, y, deg_g, deg_lg, pm_pd):
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pmpd_x = F.embedding(pm_pd, x)
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sum_x = sum(theta(z) for theta, z in zip(self.theta_list, self.aggregate(g, x)))
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g.set_e_repr({'y' : y})
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g.update_all(fn.copy_edge(edge='y', out='m'), fn.sum('m', 'pmpd_y'))
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pmpd_y = g.pop_n_repr('pmpd_y')
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x = self.theta_x(x) + self.theta_deg(deg_g * x) + sum_x + self.theta_y(pmpd_y)
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n = self.out_feats // 2
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x = th.cat([x[:, :n], F.relu(x[:, n:])], 1)
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x = self.bn_x(x)
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sum_y = sum(gamma(z) for gamma, z in zip(self.gamma_list, self.aggregate(lg, y)))
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y = self.gamma_y(y) + self.gamma_deg(deg_lg * y) + sum_y + self.gamma_x(pmpd_x)
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y = th.cat([y[:, :n], F.relu(y[:, n:])], 1)
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y = self.bn_y(y)
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return x, y
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class GNN(nn.Module):
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def __init__(self, feats, radius, n_classes):
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"""
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Parameters
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----------
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g : networkx.DiGraph
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"""
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super(GNN, self).__init__()
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self.linear = nn.Linear(feats[-1], n_classes)
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self.module_list = nn.ModuleList([GNNModule(m, n, radius)
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for m, n in zip(feats[:-1], feats[1:])])
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def forward(self, g, lg, deg_g, deg_lg, pm_pd):
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x, y = deg_g, deg_lg
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for module in self.module_list:
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x, y = module(g, lg, x, y, deg_g, deg_lg, pm_pd)
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return self.linear(x)
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