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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act, bias=True):
super(GCN, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU() if act == 'prelu' else act
if bias:
self.bias = nn.Parameter(torch.FloatTensor(out_ft))
self.bias.data.fill_(0.0)
else:
self.register_parameter('bias', None)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0.0)
def forward(self, seq, adj, sparse=False):
seq_fts = self.fc(seq)
if sparse:
out = torch.unsqueeze(torch.spmm(adj, torch.squeeze(seq_fts, 0)), 0)
else:
out = torch.bmm(adj, seq_fts)
if self.bias is not None:
out += self.bias
return self.act(out)
class AvgReadout(nn.Module):
def __init__(self):
super(AvgReadout, self).__init__()
def forward(self, seq):
return torch.mean(seq, 1)
class MaxReadout(nn.Module):
def __init__(self):
super(MaxReadout, self).__init__()
def forward(self, seq):
return torch.max(seq,1).values
class MinReadout(nn.Module):
def __init__(self):
super(MinReadout, self).__init__()
def forward(self, seq):
return torch.min(seq, 1).values
class WSReadout(nn.Module):
def __init__(self):
super(WSReadout, self).__init__()
def forward(self, seq, query):
query = query.permute(0,2,1)
sim = torch.matmul(seq,query)
sim = F.softmax(sim,dim=1)
sim = sim.repeat(1, 1, 64)
out = torch.mul(seq,sim)
out = torch.sum(out,1)
return out
class Contextual_Discriminator(nn.Module):
def __init__(self, n_h, negsamp_round):
super(Contextual_Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
self.negsamp_round = negsamp_round
def weights_init(self, m):
if isinstance(m, nn.Bilinear):
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0.0)
def forward(self, c, h_pl, s_bias1=None, s_bias2=None):
scs = []
scs.append(self.f_k(h_pl, c))
c_mi = c
for _ in range(self.negsamp_round):
c_mi = torch.cat((c_mi[-2:-1,:], c_mi[:-1,:]),0)
scs.append(self.f_k(h_pl, c_mi))
logits = torch.cat(tuple(scs))
return logits
class Patch_Discriminator(nn.Module):
def __init__(self, n_h, negsamp_round):
super(Patch_Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
self.negsamp_round = negsamp_round
def weights_init(self, m):
if isinstance(m, nn.Bilinear):
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0.0)
def forward(self, h_ano, h_unano, s_bias1=None, s_bias2=None):
scs = []
scs.append(self.f_k(h_unano, h_ano))
h_mi = h_ano
for _ in range(self.negsamp_round):
h_mi = torch.cat((h_mi[-2:-1, :], h_mi[:-1, :]), 0)
scs.append(self.f_k(h_unano, h_mi))
logits = torch.cat(tuple(scs))
return logits
class Model(nn.Module):
def __init__(self, n_in, n_h, activation, negsamp_round_patch, negsamp_round_context, readout):
super(Model, self).__init__()
self.read_mode = readout
self.gcn_context = GCN(n_in, n_h, activation)
self.gcn_patch = GCN(n_in, n_h, activation)
if readout == 'max':
self.read = MaxReadout()
elif readout == 'min':
self.read = MinReadout()
elif readout == 'avg':
self.read = AvgReadout()
elif readout == 'weighted_sum':
self.read = WSReadout()
self.c_disc = Contextual_Discriminator(n_h, negsamp_round_context)
self.p_disc = Patch_Discriminator(n_h, negsamp_round_patch)
def forward(self, seq1, adj, sparse=False, msk=None, samp_bias1=None, samp_bias2=None):
h_1 = self.gcn_context(seq1, adj, sparse)
h_2 = self.gcn_patch(seq1, adj, sparse)
if self.read_mode != 'weighted_sum':
c = self.read(h_1[:, :-1, :])
h_mv = h_1[:, -1, :]
h_unano = h_2[:, -1, :]
h_ano = h_2[:, -2, :]
else:
c = self.read(h_1[:, :-1, :], h_1[:, -2:-1, :])
h_mv = h_1[:, -1, :]
h_unano = h_2[:, -1, :]
h_ano = h_2[:, -2, :]
ret1 = self.c_disc(c, h_mv, samp_bias1, samp_bias2)
ret2 = self.p_disc(h_ano, h_unano, samp_bias1, samp_bias2)
return ret1, ret2