-
Notifications
You must be signed in to change notification settings - Fork 0
/
models.py
228 lines (187 loc) · 9.52 KB
/
models.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class BiLSTM(nn.Module):
def __init__(self, vocab_size, pretrain_emb, embedding_size=128, hidden_size=32, dropout=0.2,
multiple=0, use_seq_num=2):
super(BiLSTM, self).__init__()
self.vocab_size = vocab_size
# embedding layer
self.embed_seq = nn.Embedding(self.vocab_size + 1, embedding_size)
self.embed_keyword_seq = nn.Embedding(self.vocab_size + 1, embedding_size)
self.embed_seq.weight = nn.Parameter(pretrain_emb, requires_grad=False)
self.embed_keyword_seq.weight = nn.Parameter(pretrain_emb, requires_grad=False)
self.embed_seq.weight.requires_grad = False
self.embed_keyword_seq.weight.requires_grad = False
# LSTM layer
self.lstm_seq1 = nn.LSTM(input_size=embedding_size, hidden_size=hidden_size, dropout=dropout, batch_first=True)
self.lstm_seq2 = nn.LSTM(input_size=hidden_size, hidden_size=hidden_size, dropout=dropout, batch_first=True)
self.lstm_key_seq1 = nn.LSTM(input_size=embedding_size, hidden_size=hidden_size, dropout=dropout,
batch_first=True)
self.lstm_key_seq2 = nn.LSTM(input_size=hidden_size, hidden_size=hidden_size, dropout=dropout, batch_first=True)
self.output = nn.Sequential(
nn.Linear(6 * hidden_size + 3 * multiple, 64),
nn.ReLU(),
nn.Linear(64, 16),
nn.ReLU()
)
self.output_for_one_seq = nn.Sequential(
nn.Linear(3 * hidden_size + 3 * multiple, 64),
nn.ReLU(),
nn.Linear(64, 16),
nn.ReLU()
)
self.use_seq_num = use_seq_num
self.output_final = nn.Linear(16, 2)
def forward(self, x1, x2, x1_keywords, x2_keywords):
x1 = self.embed_seq(x1)
x2 = self.embed_seq(x2)
x1_keywords = self.embed_keyword_seq(x1_keywords)
x2_keywords = self.embed_keyword_seq(x2_keywords)
x1, _ = self.lstm_seq1(x1)
x1, _ = self.lstm_seq2(x1)
x2, _ = self.lstm_seq1(x2)
x2, _ = self.lstm_seq2(x2)
x1_keywords, _ = self.lstm_key_seq1(x1_keywords)
x1_keywords, _ = self.lstm_key_seq2(x1_keywords)
x2_keywords, _ = self.lstm_key_seq1(x2_keywords)
x2_keywords, _ = self.lstm_key_seq2(x2_keywords)
minus = x1_keywords[:, -1, :] - x2_keywords[:, -1, :]
minus_key = x1[:, -1, :] - x2[:, -1, :]
if self.use_seq_num == 2:
concat_input = torch.cat(
(minus,
minus_key,
x1[:, -1, :],
x2[:, -1, :],
x1_keywords[:, -1, :],
x2_keywords[:, -1, :],
), dim=1)
output_hidden = self.output(concat_input)
else:
concat_input = torch.cat(
(minus_key,
x1[:, -1, :],
x2[:, -1, :],
), dim=1)
output_hidden = self.output_for_one_seq(concat_input)
output = self.output_final(output_hidden)
return torch.log_softmax(output, dim=1)
class CNNMatchModel(nn.Module):
def __init__(self, input_matrix_size1, input_matrix_size2, mat1_channel1, mat1_kernel_size1,
mat1_channel2, mat1_kernel_size2, mat2_channel1, mat2_kernel_size1, hidden1,
hidden2):
super(CNNMatchModel, self).__init__()
self.mat_size1 = input_matrix_size1
self.mat_size2 = input_matrix_size2
self.conv1_1 = nn.Conv2d(1, mat1_channel1,
mat1_kernel_size1) # n*mat1_channel1*(input_matrix_size1-mat1_kernel_size1+1)*(input_matrix_size1-mat1_kernel_size1+1)
self.conv1_2 = nn.Conv2d(mat1_channel1, mat1_channel2,
mat1_kernel_size2) # n*mat1_channel2*(input_matrix_size1-mat1_kernel_size1-mat1_kernel_size2+2)*(input_matrix_size1-mat1_kernel_size1-mat1_kernel_size2+2)
self.mat1_flatten_dim = mat1_channel2 * ((input_matrix_size1 - mat1_kernel_size1 - mat1_kernel_size2 + 2) ** 2)
self.conv2_1 = nn.Conv2d(1, mat2_channel1,
mat2_kernel_size1) # n*mat2_channel1*(input_matrix_size2-mat2_kernel_size1+1)*(input_matrix_size2-mat2_kernel_size1+1)
self.mat2_flatten_dim = mat2_channel1 * ((input_matrix_size2 - mat2_kernel_size1 + 1) ** 2)
print("flat cnn", self.mat1_flatten_dim, self.mat2_flatten_dim)
self.fc_out = nn.Sequential(
nn.Linear(self.mat1_flatten_dim + self.mat2_flatten_dim, hidden1),
nn.ReLU(),
nn.Linear(hidden1, hidden2),
nn.ReLU(),
nn.Linear(hidden2, 2),
)
def forward(self, batch_matrix1, batch_matrix2):
batch_matrix1 = batch_matrix1.unsqueeze(1)
batch_matrix2 = batch_matrix2.unsqueeze(1)
mat1 = F.relu(self.conv1_1(batch_matrix1))
mat1 = F.relu(self.conv1_2(mat1))
mat1 = mat1.view(-1, self.mat1_flatten_dim)
mat2 = F.relu(self.conv2_1(batch_matrix2))
mat2 = mat2.view(-1, self.mat2_flatten_dim)
hidden = torch.cat((mat1, mat2), 1)
out = self.fc_out(hidden)
return F.log_softmax(out, dim=1)
class BatchMultiHeadGraphAttention(nn.Module):
def __init__(self, n_head, f_in, f_out, attn_dropout, n_type_nodes, bias=True):
super(BatchMultiHeadGraphAttention, self).__init__()
self.n_head = n_head
self.n_type_nodes = n_type_nodes
self.w = Parameter(torch.Tensor(n_head, f_in, f_out))
self.a_src = Parameter(torch.Tensor(n_head, f_out, self.n_type_nodes))
self.a_dst = Parameter(torch.Tensor(n_head, f_out, self.n_type_nodes))
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
self.softmax = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(attn_dropout)
if bias:
self.bias = Parameter(torch.Tensor(f_out))
nn.init.constant_(self.bias, 0)
else:
self.register_parameter('bias', None)
nn.init.xavier_uniform_(self.w)
nn.init.xavier_uniform_(self.a_src)
nn.init.xavier_uniform_(self.a_dst)
def forward(self, h, adj, v_types):
bs, n = h.size()[:2] # h is of size bs x n x f_in
h_prime = torch.matmul(h.unsqueeze(1), self.w) # bs x n_head x n x f_out
v_types = v_types.unsqueeze(1)
v_types = v_types.expand(-1, self.n_head, -1, -1)
attn_src = torch.matmul(torch.tanh(h_prime), self.a_src) # bs x n_head x n x 1
attn_src = torch.sum(torch.mul(attn_src, v_types), dim=3, keepdim=True)
attn_dst = torch.matmul(torch.tanh(h_prime), self.a_dst) # bs x n_head x n x 1
attn_dst = torch.sum(torch.mul(attn_dst, v_types), dim=3, keepdim=True)
attn = attn_src.expand(-1, -1, -1, n) + attn_dst.expand(-1, -1, -1, n).permute(0, 1, 3, 2) # bs x n_head x n x n
attn = self.leaky_relu(attn)
mask = 1 - adj.unsqueeze(1) # bs x 1 x n x n
attn.data.masked_fill_(mask.bool(), float("-inf"))
attn = self.softmax(attn) # bs x n_head x n x n
attn = self.dropout(attn)
output = torch.matmul(attn, h_prime) # bs x n_head x n x f_out
if self.bias is not None:
return output + self.bias
else:
return output
class MatchBatchHGAT(nn.Module):
def __init__(self, n_type_nodes, n_units=[1433, 8, 7], n_head=8, dropout=0.1,
attn_dropout=0.0, instance_normalization=False):
super(MatchBatchHGAT, self).__init__()
self.n_layer = len(n_units) - 1
self.dropout = dropout
self.inst_norm = instance_normalization
if self.inst_norm:
self.norm = nn.InstanceNorm1d(n_units[0], momentum=0.0, affine=True)
d_hidden = n_units[-1]
self.fc1 = torch.nn.Linear(d_hidden * 2, d_hidden * 3)
self.fc2 = torch.nn.Linear(d_hidden * 3, d_hidden)
self.fc3 = torch.nn.Linear(d_hidden, 2)
self.attentions = BatchMultiHeadGraphAttention(n_head=n_head,
f_in=n_units[0],
f_out=n_units[1],
attn_dropout=attn_dropout,
n_type_nodes=n_type_nodes)
self.out_att = BatchMultiHeadGraphAttention(n_head=1,
f_in=n_head * n_units[1],
f_out=n_units[2],
attn_dropout=attn_dropout,
n_type_nodes=n_type_nodes)
def forward(self, emb, adj, v_types, x_stat):
if self.inst_norm:
emb = self.norm(emb.transpose(1, 2)).transpose(1, 2)
bs, n = adj.size()[:2]
x = self.attentions(emb, adj, v_types)
x = F.elu(x.transpose(1, 2).contiguous().view(bs, n, -1))
x = F.dropout(x, self.dropout, training=self.training)
x = self.out_att(x, adj, v_types)
x = F.elu(x)
x = x.squeeze()
left_hidden = x[:, 0, :]
right_hidden = x[:, 1, :]
v_sim_mul = torch.mul(left_hidden, right_hidden)
x_stat = torch.cat((x_stat, x_stat, x_stat, x_stat), dim=1)
v_sim_mul = torch.cat((v_sim_mul, x_stat), dim=1)
v_sim = self.fc1(v_sim_mul)
v_sim = F.relu(v_sim)
v_sim = self.fc2(v_sim)
v_sim = F.relu(v_sim)
scores = self.fc3(v_sim)
return F.log_softmax(scores, dim=1)