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dagnn.py
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dagnn.py
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#代码参考:https://github.com/divelab/DeeperGNN/blob/master/DeeperGNN/dagnn.py
import torch
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.utils import add_remaining_self_loops,to_networkx
from torch_geometric.nn.conv import MessagePassing
from torch_scatter import scatter_add
def gcn_norm(edge_index, edge_weight=None, num_nodes=None, improved=False,
add_self_loops=True, dtype=None):
fill_value = 2. if improved else 1.
num_nodes = int(edge_index.max()) + 1 if num_nodes is None else num_nodes
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ), dtype=dtype,
device=edge_index.device)
if add_self_loops:
edge_index, tmp_edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
assert tmp_edge_weight is not None
edge_weight = tmp_edge_weight
row, col = edge_index[0], edge_index[1]
deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow_(-0.5)
deg_inv_sqrt.masked_fill_(deg_inv_sqrt == float('inf'), 0)
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
class Prop(MessagePassing):
def __init__(self, num_classes, K, **kwargs):
super(Prop, self).__init__(aggr='add', **kwargs)
self.K = K
self.proj = Linear(num_classes, 1)
def forward(self, x, edge_index, edge_weight=None):
edge_index, norm = gcn_norm(edge_index, edge_weight, x.size(0), dtype=x.dtype)
preds = []
preds.append(x)
for _ in range(self.K):
x = self.propagate(edge_index, x=x, norm=norm)
preds.append(x)
pps = torch.stack(preds, dim=1)
retain_score = self.proj(pps)
retain_score = retain_score.squeeze()
retain_score = torch.sigmoid(retain_score)
retain_score = retain_score.unsqueeze(1)
out = torch.matmul(retain_score, pps).squeeze()
return out
def message(self, x_j, norm):
#x_j: [E, num_classes]
return norm.view(-1, 1) * x_j
def __repr__(self):
return '{}(K={})'.format(self.__class__.__name__, self.K)
def reset_parameters(self):
self.proj.reset_parameters()
class DAGNN(torch.nn.Module):
def __init__(self,input_dim,hidden_dim,output_dim,K,dropout_rate):
super(DAGNN, self).__init__()
self.lin1 = Linear(input_dim, hidden_dim)
self.lin2 = Linear(hidden_dim,output_dim)
self.prop = Prop(output_dim,K)
self.dropout_rate=dropout_rate
def reset_parameters(self):
self.lin1.reset_parameters()
self.lin2.reset_parameters()
self.prop.reset_parameters()
def forward(self,x,edge_index):
x = F.dropout(x, p=self.dropout_rate, training=self.training)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=self.dropout_rate, training=self.training)
x = self.lin2(x)
x = self.prop(x, edge_index)
return {'out':F.log_softmax(x, dim=1),'emb':x}