dmlc--dgl
Mufei Li
74 行
2.8 KiB
Python
74 行
2.8 KiB
Python
import torch
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import torch.nn as nn
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from dgl.nn.pytorch import SumPooling, AvgPooling, MaxPooling, GlobalAttentionPooling, Set2Set
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from conv import GNN_node, GNN_node_Virtualnode
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class GNN(nn.Module):
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def __init__(self, num_tasks = 1, num_layers = 5, emb_dim = 300, gnn_type = 'gin',
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virtual_node = True, residual = False, drop_ratio = 0, JK = "last",
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graph_pooling = "sum"):
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'''
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num_tasks (int): number of labels to be predicted
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virtual_node (bool): whether to add virtual node or not
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'''
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super(GNN, self).__init__()
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self.num_layers = num_layers
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self.drop_ratio = drop_ratio
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self.JK = JK
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self.emb_dim = emb_dim
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self.num_tasks = num_tasks
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self.graph_pooling = graph_pooling
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if self.num_layers < 2:
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raise ValueError("Number of GNN layers must be greater than 1.")
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### GNN to generate node embeddings
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if virtual_node:
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self.gnn_node = GNN_node_Virtualnode(num_layers, emb_dim, JK = JK,
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drop_ratio = drop_ratio,
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residual = residual,
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gnn_type = gnn_type)
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else:
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self.gnn_node = GNN_node(num_layers, emb_dim, JK = JK, drop_ratio = drop_ratio,
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residual = residual, gnn_type = gnn_type)
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### Pooling function to generate whole-graph embeddings
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if self.graph_pooling == "sum":
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self.pool = SumPooling()
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elif self.graph_pooling == "mean":
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self.pool = AvgPooling()
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elif self.graph_pooling == "max":
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self.pool = MaxPooling
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elif self.graph_pooling == "attention":
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self.pool = GlobalAttentionPooling(
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gate_nn = nn.Sequential(nn.Linear(emb_dim, 2*emb_dim),
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nn.BatchNorm1d(2*emb_dim),
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nn.ReLU(),
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nn.Linear(2*emb_dim, 1)))
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elif self.graph_pooling == "set2set":
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self.pool = Set2Set(emb_dim, n_iters = 2, n_layers = 2)
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else:
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raise ValueError("Invalid graph pooling type.")
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if graph_pooling == "set2set":
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self.graph_pred_linear = nn.Linear(2*self.emb_dim, self.num_tasks)
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else:
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self.graph_pred_linear = nn.Linear(self.emb_dim, self.num_tasks)
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def forward(self, g, x, edge_attr):
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h_node = self.gnn_node(g, x, edge_attr)
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h_graph = self.pool(g, h_node)
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output = self.graph_pred_linear(h_graph)
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if self.training:
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return output
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else:
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return torch.clamp(output, min=0, max=50)
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