-
Notifications
You must be signed in to change notification settings - Fork 2
/
model.py
364 lines (290 loc) · 13.3 KB
/
model.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
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import GATConv, SAGEConv, GINEConv
from torch_geometric.nn import global_mean_pool, global_max_pool
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import BatchNorm1d, Linear, ReLU, Sequential
from torch_geometric.utils import add_self_loops, degree
allowable_synthesis_features = {
'synth_type': [0, 1, 2, 3, 4, 5, 6, 7]
}
def get_synth_feature_dims():
return list(map(len, [
allowable_synthesis_features['synth_type']
]))
full_synthesis_feature_dims = get_synth_feature_dims()
allowable_features = {
'node_type': [0, 1, 2],
'num_inverted_predecessors': [0, 1, 2],
'edge_type': [0, 1]
}
def get_node_feature_dims():
return list(map(len, [
allowable_features['node_type']
]))
def get_edge_feature_dims():
return list(map(len, [
allowable_features['edge_type']
]))
full_node_feature_dims = get_node_feature_dims()
full_edge_feature_dims = get_edge_feature_dims()
class NodeEncoder(torch.nn.Module):
def __init__(self, emb_dim):
super(NodeEncoder, self).__init__()
self.node_type_embedding = torch.nn.Embedding(full_node_feature_dims[0], emb_dim)
torch.nn.init.xavier_uniform_(self.node_type_embedding.weight.data)
def forward(self, x):
# First feature is node type, second feature is inverted predecessor
x_embedding = self.node_type_embedding(x[:, 0])
x_embedding = torch.cat((x_embedding, x[:, 1].reshape(-1, 1)), dim=1)
return x_embedding
# class SAGE(torch.nn.Module):
# def __init__(self, in_emb_dim, out_emb_dim):
# super(SAGE, self).__init__()
# self.conv1 = SAGEConv(in_emb_dim, out_emb_dim)
# self.conv2 = SAGEConv(out_emb_dim, out_emb_dim)
#
# self.batch_norm1 = torch.nn.BatchNorm1d(out_emb_dim)
# self.batch_norm2 = torch.nn.BatchNorm1d(out_emb_dim)
#
# def forward(self, h, edge_index, batch):
#
# h = F.relu(self.batch_norm1(self.conv1(h, edge_index)))
# #h = F.dropout(h, p=0.6, training=self.training)
# # h = F.relu(self.batch_norm2(self.conv2(h, edge_index)))
# h = self.batch_norm2(self.conv2(h, edge_index))
#
# xF = torch.cat([global_max_pool(h, batch), global_mean_pool(h, batch)], dim=1)
# return xF
class GINEEncoder(nn.Module):
def __init__(self, in_channels, dim, dropout):
super(GINEEncoder, self).__init__()
self.conv1 = GINEConv(
Sequential(Linear(in_channels, dim), BatchNorm1d(dim), ReLU(),
Linear(dim, dim), ReLU()))
self.conv2 = GINEConv(
Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU(),
Linear(dim, dim), ReLU()))
self.conv3 = GINEConv(
Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU(),
Linear(dim, dim), ReLU()))
self.conv4 = GINEConv(
Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU(),
Linear(dim, dim), ReLU()))
self.conv5 = GINEConv(
Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU(),
Linear(dim, 64)))
# self.conv1 = GINEConv(
# Sequential(Linear(in_channels, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv2 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv3 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv4 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv5 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv6 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv7 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv8 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv9 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()))
#
# self.conv10 = GINEConv(
# Sequential(Linear(dim, 64), BatchNorm1d(64)))
# self.conv1 = GINEConv(
# Sequential(Linear(in_channels, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv2 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv3 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv4 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv5 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv6 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv7 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv8 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv9 = GINEConv(
# Sequential(Linear(dim, dim), BatchNorm1d(dim), ReLU()), edge_dim=2)
#
# self.conv10 = GINEConv(
# Sequential(Linear(dim, 64), BatchNorm1d(64)), edge_dim=2)
self.dropout = nn.Dropout(dropout)
self.act = F.relu
self._reset_parameters()
self.attr_encode = nn.Embedding(full_edge_feature_dims[0], 32)
def _reset_parameters(self):
r"""Initiate parameters in the transformer model."""
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, data, x):
edge_index, edge_attr, batch = data.edge_index, data.edge_type, data.batch
#print('edge_attr---->', edge_attr)
#x = self.x_encode(x)
edge_attr = self.attr_encode(edge_attr.long()).squeeze()
# print('x--->', x[0].size(-1))
# print('edge_attr---->',edge_attr.size(-1))
x = self.conv1(x, edge_index, edge_attr)
x = self.conv2(x, edge_index, edge_attr)
x = self.conv3(x, edge_index, edge_attr)
x = self.conv4(x, edge_index, edge_attr)
x = self.conv5(x, edge_index, edge_attr)
# x = self.conv6(x, edge_index, edge_attr)
# x = self.conv7(x, edge_index, edge_attr)
# x = self.conv8(x, edge_index, edge_attr)
# x = self.conv9(x, edge_index, edge_attr)
# x = self.conv10(x, edge_index, edge_attr)
x = torch.cat([global_max_pool(x, batch), global_mean_pool(x, batch)], dim=1)
return x
class GNN(torch.nn.Module):
"""
Output:
node representations
"""
def __init__(self, node_encoder, input_dim, emb_dim=32, gnn_type='gcn'):
'''
emb_dim (int): node embedding dimensionality
num_layer (int): number of GNN message passing layers
'''
super(GNN, self).__init__()
self.node_emb_size = input_dim
self.node_encoder = node_encoder
node_encoder = node_encoder
self.conv_net = GINEEncoder(input_dim, emb_dim, dropout=0.1)
#self.conv2 = GCNConv(emb_dim, emb_dim)
# self.conv3 = GCNConv(emb_dim, emb_dim)
#self.batch_norm1 = torch.nn.BatchNorm1d(emb_dim)
#self.batch_norm2 = torch.nn.BatchNorm1d(emb_dim)
# self.batch_norm3 = torch.nn.BatchNorm1d(emb_dim)
def forward(self, batched_data):
# edge_index, batch, edge_attr = batched_data.edge_index, batched_data.batch, batched_data.edge_type
x = torch.cat([batched_data.node_type.reshape(-1, 1), batched_data.num_inverted_predecessors.reshape(-1, 1)],
dim=1)
x = self.node_encoder(x)
#xF = self.conv_net(h, edge_index, edge_attr, batch)
xF = self.conv_net(batched_data, x)
return xF
class SynthFlowEncoder(torch.nn.Module):
def __init__(self, emb_dim):
super(SynthFlowEncoder, self).__init__()
self.synth_emb = torch.nn.Embedding(full_synthesis_feature_dims[0], emb_dim, padding_idx=0)
torch.nn.init.xavier_uniform_(self.synth_emb.weight.data)
def forward(self, x):
"""
x_embedding = self.synth_emb(x[:, 0])
for i in range(1, x.shape[1]):
x_embedding = torch.cat((x_embedding, self.synth_emb(x[:, i])), dim=1)
"""
x_embedding = self.synth_emb(x)
return x_embedding
class SynthConv(torch.nn.Module):
def __init__(self, inp_channel=1, out_channel=3, ksize=6, stride_len=1):
super(SynthConv, self).__init__()
self.conv1d = torch.nn.Conv1d(inp_channel, out_channel, kernel_size=(ksize,), stride=(stride_len,))
def forward(self, x):
x = x.reshape(-1, 1, x.size(1)) # Convert [4,60] to [4,1,60]
x = self.conv1d(x)
return x.reshape(x.size(0), -1) # Convert [4,3,55] to [4,165]
class SynthNet(torch.nn.Module):
def __init__(self, node_encoder, synth_encoder, n_classes, synth_input_dim, node_input_dim, gnn_embed_dim=128,
num_fc_layer=4, hidden_dim=512, channels=4, dropout=0.2):
super(SynthNet, self).__init__()
self.num_layers = num_fc_layer
self.hidden_dim = hidden_dim
self.node_encoder = node_encoder
self.synth_encoder = synth_encoder
self.node_enc_outdim = node_input_dim
self.synth_enc_outdim = synth_input_dim
self.gnn_emb_dim = gnn_embed_dim
self.n_classes = n_classes
self.dropout = dropout
self.input_pos_embedding = torch.nn.Embedding(20, embedding_dim=channels)
self.target_pos_embedding = torch.nn.Embedding(full_node_feature_dims[0], embedding_dim=channels)
encoder_layer = nn.TransformerEncoderLayer(
d_model=channels,
nhead=2,
dropout=self.dropout,
dim_feedforward=8 * channels,
)
self.encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers=3)
self.input_projection = Linear(3, channels)
# self.output_projection = Linear(n_decoder_inputs, channels)
self.linear = Linear(channels, 1)
# Multiplier by 2 since each gate and node type has same encoding out dimension
# self.gnn = GNN(self.node_encoder,self.node_enc_outdim*2)
# Node encoding has dimension 3 and number of incoming inverted edges has dimension 1
self.gnn = GNN(self.node_encoder, self.node_enc_outdim + 1)
self.fcs = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
# GNN + (synthesis flow encoding + synthesis convolution)
self.in_dim_to_fcs = int(self.gnn_emb_dim + 50)
self.fcs.append(torch.nn.Linear(self.in_dim_to_fcs, self.hidden_dim))
for layer in range(1, self.num_layers - 1):
self.fcs.append(torch.nn.Linear(self.hidden_dim, self.hidden_dim))
self.fcs.append(torch.nn.Linear(self.hidden_dim, self.n_classes))
self.syn_linear = Linear(80, 50)
def encode_src(self, src,mask):
#print('src_size---->', src.size())
# src_start = self.input_projection(src).permute(1, 0, 2)
src_start = src.permute(1, 0, 2)
in_sequence_len, batch_size = src_start.size(0), src_start.size(1)
pos_encoder = (
torch.arange(0, in_sequence_len, device=src.device)
.unsqueeze(0)
.repeat(batch_size, 1)
)
pos_encoder = self.input_pos_embedding(pos_encoder).permute(1, 0, 2)
src = src_start + pos_encoder
#print('src---->',src)
#print('pos_encoder---->',pos_encoder)
src = self.encoder(src,src_key_padding_mask=mask) + src_start
return src
def forward(self, batch_data):
graphEmbed = self.gnn(batch_data)
synthFlow = batch_data.synVec
flow = synthFlow.reshape(-1, 20)
#print(flow.size())
mask = (flow == 0).reshape(flow.shape[0], flow.shape[1])
#print('mask---->',mask)
h_syn = self.synth_encoder(flow)
src = h_syn
#print('src---->', src.size())
synconv1_out = self.encode_src(src,mask)
synconv1_out = synconv1_out.permute(1, 0, 2)
synconv1_out = synconv1_out.reshape(synconv1_out.size(0), -1)
# print('synconv1_out---->', synconv1_out.size())
synconv1_out = self.syn_linear(synconv1_out)
concatenatedInput = torch.cat([graphEmbed, synconv1_out], dim=1)
# concatenatedInput = torch.cat([graphEmbed, synconv1_out, synconv2_out, synconv3_out], dim=1)
x = F.relu(self.fcs[0](concatenatedInput))
x = F.dropout(x, p=self.dropout, training=self.training)
for layer in range(1, self.num_layers - 1):
x = F.relu(self.fcs[layer](x))
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.fcs[-1](x)
return x