# -*- coding: utf-8 -*-
#
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
#
# MPNN
# pylint: disable= no-member, arguments-differ, invalid-name
import torch.nn as nn
from dgl.nn.pytorch import Set2Set
from ..gnn import MPNNGNN
__all__ = ['MPNNPredictor']
# pylint: disable=W0221
[docs]class MPNNPredictor(nn.Module):
"""MPNN for regression and classification on graphs.
MPNN is introduced in `Neural Message Passing for Quantum Chemistry
<https://arxiv.org/abs/1704.01212>`__.
Parameters
----------
node_in_feats : int
Size for the input node features.
edge_in_feats : int
Size for the input edge features.
node_out_feats : int
Size for the output node representations. Default to 64.
edge_hidden_feats : int
Size for the hidden edge representations. Default to 128.
n_tasks : int
Number of tasks, which is also the output size. Default to 1.
num_step_message_passing : int
Number of message passing steps. Default to 6.
num_step_set2set : int
Number of set2set steps. Default to 6.
num_layer_set2set : int
Number of set2set layers. Default to 3.
"""
def __init__(self,
node_in_feats,
edge_in_feats,
node_out_feats=64,
edge_hidden_feats=128,
n_tasks=1,
num_step_message_passing=6,
num_step_set2set=6,
num_layer_set2set=3):
super(MPNNPredictor, self).__init__()
self.gnn = MPNNGNN(node_in_feats=node_in_feats,
node_out_feats=node_out_feats,
edge_in_feats=edge_in_feats,
edge_hidden_feats=edge_hidden_feats,
num_step_message_passing=num_step_message_passing)
self.readout = Set2Set(input_dim=node_out_feats,
n_iters=num_step_set2set,
n_layers=num_layer_set2set)
self.predict = nn.Sequential(
nn.Linear(2 * node_out_feats, node_out_feats),
nn.ReLU(),
nn.Linear(node_out_feats, n_tasks)
)
[docs] def forward(self, g, node_feats, edge_feats):
"""Graph-level regression/soft classification.
Parameters
----------
g : DGLGraph
DGLGraph for a batch of graphs.
node_feats : float32 tensor of shape (V, node_in_feats)
Input node features.
edge_feats : float32 tensor of shape (E, edge_in_feats)
Input edge features.
Returns
-------
float32 tensor of shape (G, n_tasks)
Prediction for the graphs in the batch. G for the number of graphs.
"""
node_feats = self.gnn(g, node_feats, edge_feats)
graph_feats = self.readout(g, node_feats)
return self.predict(graph_feats)