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MessageFunction.py
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MessageFunction.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
MessageFunction.py: Propagates a message depending on two nodes and their common edge.
Usage:
"""
from __future__ import print_function
# Own modules
import datasets
from models.nnet import NNet
import numpy as np
import os
import argparse
import time
import torch
import torch.nn as nn
from torch.autograd.variable import Variable
__author__ = "Pau Riba, Anjan Dutta"
__email__ = "[email protected], [email protected]"
class MessageFunction(nn.Module):
# Constructor
def __init__(self, message_def='duvenaud', args={}):
super(MessageFunction, self).__init__()
self.m_definition = ''
self.m_function = None
self.args = {}
self.__set_message(message_def, args)
# Message from h_v to h_w through e_vw
def forward(self, h_v, h_w, e_vw, args=None):
return self.m_function(h_v, h_w, e_vw, args)
# Set a message function
def __set_message(self, message_def, args={}):
self.m_definition = message_def.lower()
self.m_function = {
'duvenaud': self.m_duvenaud,
'ggnn': self.m_ggnn,
'intnet': self.m_intnet,
'mpnn': self.m_mpnn,
'mgc': self.m_mgc,
'bruna': self.m_bruna,
'defferrard': self.m_deff,
'kipf': self.m_kipf
}.get(self.m_definition, None)
if self.m_function is None:
print('WARNING!: Message Function has not been set correctly\n\tIncorrect definition ' + message_def)
quit()
init_parameters = {
'duvenaud': self.init_duvenaud,
'ggnn': self.init_ggnn,
'intnet': self.init_intnet,
'mpnn': self.init_mpnn
}.get(self.m_definition, lambda x: (nn.ParameterList([]), nn.ModuleList([]), {}))
self.learn_args, self.learn_modules, self.args = init_parameters(args)
self.m_size = {
'duvenaud': self.out_duvenaud,
'ggnn': self.out_ggnn,
'intnet': self.out_intnet,
'mpnn': self.out_mpnn
}.get(self.m_definition, None)
# Get the name of the used message function
def get_definition(self):
return self.m_definition
# Get the message function arguments
def get_args(self):
return self.args
# Get Output size
def get_out_size(self, size_h, size_e, args=None):
return self.m_size(size_h, size_e, args)
# Definition of various state of the art message functions
# Duvenaud et al. (2015), Convolutional Networks for Learning Molecular Fingerprints
def m_duvenaud(self, h_v, h_w, e_vw, args):
m = torch.cat([h_w, e_vw], 2)
return m
def out_duvenaud(self, size_h, size_e, args):
return size_h + size_e
def init_duvenaud(self, params):
learn_args = []
learn_modules = []
args = {}
return nn.ParameterList(learn_args), nn.ModuleList(learn_modules), args
# Li et al. (2016), Gated Graph Neural Networks (GG-NN)
def m_ggnn(self, h_v, h_w, e_vw, opt={}):
m = Variable(torch.zeros(h_w.size(0), h_w.size(1), self.args['out']).type_as(h_w.data))
for w in range(h_w.size(1)):
if torch.nonzero(e_vw[:, w, :].data).size():
for i, el in enumerate(self.args['e_label']):
ind = (el == e_vw[:,w,:]).type_as(self.learn_args[0][i])
parameter_mat = self.learn_args[0][i][None, ...].expand(h_w.size(0), self.learn_args[0][i].size(0),
self.learn_args[0][i].size(1))
m_w = torch.transpose(torch.bmm(torch.transpose(parameter_mat, 1, 2),
torch.transpose(torch.unsqueeze(h_w[:, w, :], 1),
1, 2)), 1, 2)
m_w = torch.squeeze(m_w)
m[:,w,:] = ind.expand_as(m_w)*m_w
return m
def out_ggnn(self, size_h, size_e, args):
return self.args['out']
def init_ggnn(self, params):
learn_args = []
learn_modules = []
args = {}
args['e_label'] = params['e_label']
args['in'] = params['in']
args['out'] = params['out']
# Define a parameter matrix A for each edge label.
learn_args.append(nn.Parameter(torch.randn(len(params['e_label']), params['in'], params['out'])))
return nn.ParameterList(learn_args), nn.ModuleList(learn_modules), args
# Battaglia et al. (2016), Interaction Networks
def m_intnet(self, h_v, h_w, e_vw, args):
m = torch.cat([h_v[:, None, :].expand_as(h_w), h_w, e_vw], 2)
b_size = m.size()
m = m.view(-1, b_size[2])
m = self.learn_modules[0](m)
m = m.view(b_size[0], b_size[1], -1)
return m
def out_intnet(self, size_h, size_e, args):
return self.args['out']
def init_intnet(self, params):
learn_args = []
learn_modules = []
args = {}
args['in'] = params['in']
args['out'] = params['out']
learn_modules.append(NNet(n_in=params['in'], n_out=params['out']))
return nn.ParameterList(learn_args), nn.ModuleList(learn_modules), args
# Gilmer et al. (2017), Neural Message Passing for Quantum Chemistry
def m_mpnn(self, h_v, h_w, e_vw, opt={}):
# Matrices for each edge
edge_output = self.learn_modules[0](e_vw)
edge_output = edge_output.view(-1, self.args['out'], self.args['in'])
h_w_rows = h_w[..., None].expand(h_w.size(0), h_v.size(1), h_w.size(1)).contiguous()
h_w_rows = h_w_rows.view(-1, self.args['in'])
h_multiply = torch.bmm(edge_output, torch.unsqueeze(h_w_rows,2))
m_new = torch.squeeze(h_multiply)
return m_new
def out_mpnn(self, size_h, size_e, args):
return self.args['out']
def init_mpnn(self, params):
learn_args = []
learn_modules = []
args = {}
args['in'] = params['in']
args['out'] = params['out']
# Define a parameter matrix A for each edge label.
learn_modules.append(NNet(n_in=params['edge_feat'], n_out=(params['in']*params['out'])))
return nn.ParameterList(learn_args), nn.ModuleList(learn_modules), args
# Kearnes et al. (2016), Molecular Graph Convolutions
def m_mgc(self, h_v, h_w, e_vw, args):
m = e_vw
return m
# Laplacian based methods
# Bruna et al. (2013)
def m_bruna(self, h_v, h_w, e_vw, args):
# TODO
m = []
return m
# Defferrard et al. (2016)
def m_deff(self, h_v, h_w, e_vw, args):
# TODO
m = []
return m
# Kipf & Welling (2016)
def m_kipf(self, h_v, h_w, e_vw, args):
# TODO
m = []
return m
if __name__ == '__main__':
# Parse optios for downloading
parser = argparse.ArgumentParser(description='QM9 Object.')
# Optional argument
parser.add_argument('--root', nargs=1, help='Specify the data directory.', default=['./data/qm9/dsgdb9nsd/'])
args = parser.parse_args()
root = args.root[0]
files = [f for f in os.listdir(root) if os.path.isfile(os.path.join(root, f))]
idx = np.random.permutation(len(files))
idx = idx.tolist()
valid_ids = [files[i] for i in idx[0:10000]]
test_ids = [files[i] for i in idx[10000:20000]]
train_ids = [files[i] for i in idx[20000:]]
data_train = datasets.Qm9(root, train_ids)
data_valid = datasets.Qm9(root, valid_ids)
data_test = datasets.Qm9(root, test_ids)
# Define message
m = MessageFunction('duvenaud')
print(m.get_definition())
start = time.time()
# Select one graph
g_tuple, l = data_train[0]
g, h_t, e = g_tuple
m_t = {}
for v in g.nodes_iter():
neigh = g.neighbors(v)
m_neigh = type(h_t)
for w in neigh:
if (v,w) in e:
e_vw = e[(v, w)]
else:
e_vw = e[(w, v)]
m_v = m.forward(h_t[v], h_t[w], e_vw)
if len(m_neigh):
m_neigh += m_v
else:
m_neigh = m_v
m_t[v] = m_neigh
end = time.time()
print('Input nodes')
print(h_t)
print('Message')
print(m_t)
print('Time')
print(end - start)