import numpy as np
import torch
import torch.nn as nn
from .. import BaseModel
from cogdl.utils import get_activation, spmm
# Borrowed from https://github.com/PetarV-/DGI
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act, bias=True):
super(GCN, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU() if act == "prelu" else get_activation(act)
if bias:
self.bias = nn.Parameter(torch.FloatTensor(out_ft))
self.bias.data.fill_(0.0)
else:
self.register_parameter("bias", None)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0.0)
# Shape of seq: (batch, nodes, features)
def forward(self, graph, seq, sparse=False):
seq_fts = self.fc(seq)
if len(seq_fts.shape) > 2:
if sparse:
out = torch.unsqueeze(spmm(graph, torch.squeeze(seq_fts, 0)), 0)
else:
out = torch.bmm(graph, seq_fts)
else:
if sparse:
out = spmm(graph, torch.squeeze(seq_fts, 0))
else:
out = torch.mm(graph, seq_fts)
if self.bias is not None:
out += self.bias
return self.act(out)
[docs]class DGIModel(BaseModel):
[docs] @staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument("--hidden-size", type=int, default=512)
parser.add_argument("--activation", type=str, default="prelu")
# fmt: on
[docs] @classmethod
def build_model_from_args(cls, args):
return cls(args.num_features, args.hidden_size, args.activation)
def __init__(self, in_feats, hidden_size, activation):
super(DGIModel, self).__init__()
self.gcn = GCN(in_feats, hidden_size, activation)
self.sparse = True
[docs] def forward(self, graph):
graph.sym_norm()
x = graph.x
logits = self.gcn(graph, x, self.sparse)
return logits
# Detach the return variables
[docs] def embed(self, data):
h_1 = self.gcn(data, data.x, self.sparse)
return h_1.detach()