wehub-resource-sync
44 lines
1.2 KiB
Python
Executable File
44 lines
1.2 KiB
Python
Executable File
import torch
|
|
from torch import nn as nn
|
|
from torch.nn import functional as F
|
|
|
|
|
|
class BatchedGraphSAGE(nn.Module):
|
|
def __init__(
|
|
self, infeat, outfeat, use_bn=True, mean=False, add_self=False
|
|
):
|
|
super().__init__()
|
|
self.add_self = add_self
|
|
self.use_bn = use_bn
|
|
self.mean = mean
|
|
self.W = nn.Linear(infeat, outfeat, bias=True)
|
|
|
|
nn.init.xavier_uniform_(
|
|
self.W.weight, gain=nn.init.calculate_gain("relu")
|
|
)
|
|
|
|
def forward(self, x, adj):
|
|
num_node_per_graph = adj.size(1)
|
|
if self.use_bn and not hasattr(self, "bn"):
|
|
self.bn = nn.BatchNorm1d(num_node_per_graph).to(adj.device)
|
|
|
|
if self.add_self:
|
|
adj = adj + torch.eye(num_node_per_graph).to(adj.device)
|
|
|
|
if self.mean:
|
|
adj = adj / adj.sum(-1, keepdim=True)
|
|
|
|
h_k_N = torch.matmul(adj, x)
|
|
h_k = self.W(h_k_N)
|
|
h_k = F.normalize(h_k, dim=2, p=2)
|
|
h_k = F.relu(h_k)
|
|
if self.use_bn:
|
|
h_k = self.bn(h_k)
|
|
return h_k
|
|
|
|
def __repr__(self):
|
|
if self.use_bn:
|
|
return "BN" + super(BatchedGraphSAGE, self).__repr__()
|
|
else:
|
|
return super(BatchedGraphSAGE, self).__repr__()
|