# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""
| Blocks for Graph Neural Network (GNN)
| Adapted from https://github.com/snap-stanford/pretrain-gnns/blob/master/chem/model.py
"""
import paddle
import paddle.nn as nn
import pgl
[docs]class GraphNorm(nn.Layer):
"""Implementation of graph normalization. Each node features is divied by sqrt(num_nodes) per graphs.
Args:
graph: the graph object from (:code:`Graph`)
feature: A tensor with shape (num_nodes, feature_size).
Return:
A tensor with shape (num_nodes, hidden_size)
References:
[1] BENCHMARKING GRAPH NEURAL NETWORKS. https://arxiv.org/abs/2003.00982
"""
def __init__(self):
super(GraphNorm, self).__init__()
self.graph_pool = pgl.nn.GraphPool(pool_type="sum")
[docs] def forward(self, graph, feature):
"""graph norm"""
nodes = paddle.ones(shape=[graph.num_nodes, 1], dtype="float32")
norm = self.graph_pool(graph, nodes)
norm = paddle.sqrt(norm)
norm = paddle.gather(norm, graph.graph_node_id)
return feature / norm
[docs]class MeanPool(nn.Layer):
"""
TODO: temporary class due to pgl mean pooling
"""
def __init__(self):
super().__init__()
self.graph_pool = pgl.nn.GraphPool(pool_type="sum")
[docs] def forward(self, graph, node_feat):
"""
mean pooling
"""
sum_pooled = self.graph_pool(graph, node_feat)
ones_sum_pooled = self.graph_pool(
graph,
paddle.ones_like(node_feat, dtype="float32"))
pooled = sum_pooled / ones_sum_pooled
return pooled
[docs]class GIN(nn.Layer):
"""
Implementation of Graph Isomorphism Network (GIN) layer with edge features
"""
def __init__(self, hidden_size):
super(GIN, self).__init__()
self.mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 2),
nn.ReLU(),
nn.Linear(hidden_size * 2, hidden_size))
[docs] def forward(self, graph, node_feat, edge_feat):
"""
Args:
node_feat(tensor): node features with shape (num_nodes, feature_size).
edge_feat(tensor): edges features with shape (num_edges, feature_size).
"""
def _send_func(src_feat, dst_feat, edge_feat):
x = src_feat['h'] + edge_feat['h']
return {'h': x}
def _recv_func(msg):
return msg.reduce_sum(msg['h'])
msg = graph.send(
message_func=_send_func,
node_feat={'h': node_feat},
edge_feat={'h': edge_feat})
node_feat = graph.recv(reduce_func=_recv_func, msg=msg)
node_feat = self.mlp(node_feat)
return node_feat