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model.py
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model.py
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import torch
import torch.nn as nn
class AttrProxy(object):
"""
Translates index lookups into attribute lookups.
To implement some trick which able to use list of nn.Module in a nn.Module
see https://discuss.pytorch.org/t/list-of-nn-module-in-a-nn-module/219/2
"""
def __init__(self, module, prefix):
self.module = module
self.prefix = prefix
def __getitem__(self, i):
return getattr(self.module, self.prefix + str(i))
class Propogator(nn.Module):
"""
Gated Propogator for GGNN
Using LSTM gating mechanism
"""
def __init__(self, state_dim, n_node, n_edge_types):
super(Propogator, self).__init__()
self.n_node = n_node
self.n_edge_types = n_edge_types
self.reset_gate = nn.Sequential(
nn.Linear(state_dim*3, state_dim),
nn.Sigmoid()
)
self.update_gate = nn.Sequential(
nn.Linear(state_dim*3, state_dim),
nn.Sigmoid()
)
self.tansform = nn.Sequential(
nn.Linear(state_dim*3, state_dim),
nn.Tanh()
)
def forward(self, state_in, state_out, state_cur, A):
A_in = A[:, :, :self.n_node*self.n_edge_types]
A_out = A[:, :, self.n_node*self.n_edge_types:]
a_in = torch.bmm(A_in, state_in)
a_out = torch.bmm(A_out, state_out)
a = torch.cat((a_in, a_out, state_cur), 2)
r = self.reset_gate(a)
z = self.update_gate(a)
joined_input = torch.cat((a_in, a_out, r * state_cur), 2)
h_hat = self.tansform(joined_input)
output = (1 - z) * state_cur + z * h_hat
return output
class GGNN(nn.Module):
"""
Gated Graph Sequence Neural Networks (GGNN)
Mode: SelectNode
Implementation based on https://arxiv.org/abs/1511.05493
"""
def __init__(self, opt):
super(GGNN, self).__init__()
assert (opt.state_dim >= opt.annotation_dim, \
'state_dim must be no less than annotation_dim')
self.state_dim = opt.state_dim
self.annotation_dim = opt.annotation_dim
self.n_edge_types = opt.n_edge_types
self.n_node = opt.n_node
self.n_steps = opt.n_steps
for i in range(self.n_edge_types):
# incoming and outgoing edge embedding
in_fc = nn.Linear(self.state_dim, self.state_dim)
out_fc = nn.Linear(self.state_dim, self.state_dim)
self.add_module("in_{}".format(i), in_fc)
self.add_module("out_{}".format(i), out_fc)
self.in_fcs = AttrProxy(self, "in_")
self.out_fcs = AttrProxy(self, "out_")
# Propogation Model
self.propogator = Propogator(self.state_dim, self.n_node, self.n_edge_types)
# Output Model
self.out = nn.Sequential(
nn.Linear(self.state_dim + self.annotation_dim, self.state_dim),
nn.Tanh(),
nn.Linear(self.state_dim, 1)
)
self._initialization()
def _initialization(self):
for m in self.modules():
if isinstance(m, nn.Linear):
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
def forward(self, prop_state, annotation, A):
for i_step in range(self.n_steps):
in_states = []
out_states = []
for i in range(self.n_edge_types):
in_states.append(self.in_fcs[i](prop_state))
out_states.append(self.out_fcs[i](prop_state))
in_states = torch.stack(in_states).transpose(0, 1).contiguous()
in_states = in_states.view(-1, self.n_node*self.n_edge_types, self.state_dim)
out_states = torch.stack(out_states).transpose(0, 1).contiguous()
out_states = out_states.view(-1, self.n_node*self.n_edge_types, self.state_dim)
prop_state = self.propogator(in_states, out_states, prop_state, A)
join_state = torch.cat((prop_state, annotation), 2)
output = self.out(join_state)
output = output.sum(2)
return output