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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""This package implements common layers to help building
graph neural networks.
"""
import paddle.fluid as fluid
from pgl import graph_wrapper
from pgl.utils import paddle_helper
from pgl.utils import op
__all__ = ['graph_pooling', 'graph_norm']
[docs]def graph_pooling(gw, node_feat, pool_type):
"""Implementation of graph pooling
This is an implementation of graph pooling
Args:
gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)
node_feat: A tensor with shape (num_nodes, feature_size).
pool_type: The type of pooling ("sum", "average" , "min")
Return:
A tensor with shape (num_graph, hidden_size)
"""
graph_feat = op.nested_lod_reset(node_feat, gw.graph_lod)
graph_feat = fluid.layers.sequence_pool(graph_feat, pool_type)
return graph_feat
[docs]def graph_norm(gw, feature):
"""Implementation of graph normalization
Reference Paper: BENCHMARKING GRAPH NEURAL NETWORKS
Each node features is divied by sqrt(num_nodes) per graphs.
Args:
gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)
feature: A tensor with shape (num_nodes, hidden_size)
Return:
A tensor with shape (num_nodes, hidden_size)
"""
nodes = fluid.layers.fill_constant(
[gw.num_nodes, 1], dtype="float32", value=1.0)
norm = graph_pooling(gw, nodes, pool_type="sum")
norm = fluid.layers.sqrt(norm)
feature_lod = op.nested_lod_reset(feature, gw.graph_lod)
norm = fluid.layers.sequence_expand_as(norm, feature_lod)
norm.stop_gradient = True
return feature_lod / norm