-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathmodels.py
246 lines (204 loc) · 8.02 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
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
import torch
import torch.nn as nn
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import GATConv
from torch_geometric.nn import InstanceNorm, BatchNorm
from torch_geometric.nn import TopKPooling, SAGPooling
from torch_geometric.nn import GlobalAttention
from torch_geometric.nn import global_max_pool, global_mean_pool
class PoolNet(torch.nn.Module):
def __init__(self, num_classes):
super(PoolNet, self).__init__()
self.att_w = nn.Sequential(
nn.Linear(256, 64),
nn.ELU(),
nn.Linear(64, 1)
)
self.att_net = nn.Sequential(
nn.Linear(256,256),
nn.ELU()
)
self.conv1 = GATConv(3, 16, heads=2)
self.norm1 = InstanceNorm(32)
self.pool1 = TopKPooling(32, ratio=0.3)
self.conv2 = GATConv(32, 64, heads=2)
self.norm2 = InstanceNorm(128)
self.pool2 = TopKPooling(128, ratio=0.3)
self.conv3 = GATConv(128, 256, heads=1)
self.norm3 = InstanceNorm(256)
self.att = GlobalAttention(gate_nn=self.att_w,
nn=self.att_net)
self.lin1 = Linear(256, 512)
self.lin2 = Linear(512, 128)
self.lin3 = Linear(128, num_classes)
def forward(self, data):
x, edge_index, batch = data.pos, data.edge_index, data.batch
#print(f'\nInput = {x.shape}, {edge_index.shape}, {batch.shape}')
x = self.conv1(x, edge_index)
x = self.norm1(x, batch)
x = F.elu(x)
#print(f'Conv1 = {x.shape}, {edge_index.shape}, {batch.shape}')
x, edge_index, _, batch, _, _ = self.pool1(x=x, edge_index=edge_index, batch=batch)
#print(f'Pool1 = {x.shape}, {edge_index.shape}, {batch.shape}')
x = self.conv2(x, edge_index)
x = self.norm2(x, batch)
x = F.elu(x)
#print(f'Conv2 = {x.shape}, {edge_index.shape}, {batch.shape}')
x, edge_index, _, batch, _, _ = self.pool2(x=x, edge_index=edge_index, batch=batch)
#print(f'Pool2 = {x.shape}, {edge_index.shape}, {batch.shape}')
x = self.conv3(x, edge_index)
x = self.norm3(x, batch)
x = F.elu(x)
#print(f'Conv3 = {x.shape}, {edge_index.shape}, {batch.shape}')
##x = global_max_pool(x, batch)
x = self.att(x, batch)
#print(f'MAXPOOL = {x.shape}, {edge_index.shape}, {batch.shape}')
x = F.dropout(x, p=0.5, training=self.training)
x = F.elu(self.lin1(x))
#print(f'Lin1 = {x.shape}')
x = F.dropout(x, p=0.5, training=self.training)
x = F.elu(self.lin2(x))
#print(f'Lin2 = {x.shape}')
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
#print(f'Lin3 = {x.shape}')
return F.log_softmax(x, dim=1)
class PoolNetv2(torch.nn.Module):
def __init__(self, num_classes):
super(PoolNetv2, self).__init__()
self.att_w = nn.Sequential(
nn.Linear(512, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 1)
)
self.att_net = nn.Sequential(
nn.Linear(512, 512),
#nn.Tanh()
nn.ELU()
)
self.conv1 = GATConv(3, 32, heads=2)
self.norm1 = InstanceNorm(64, affine=True)
self.pool1 = TopKPooling(64, ratio=0.3, nonlinearity=torch.sigmoid)
self.conv2 = GATConv(64, 128, heads=2)
self.norm2 = InstanceNorm(256, affine=True)
self.pool2 = TopKPooling(256, ratio=0.3, nonlinearity=torch.sigmoid)
self.conv3 = GATConv(256, 512, heads=2, concat=False)
self.norm3 = InstanceNorm(512, affine=True)
self.att = GlobalAttention(gate_nn=self.att_w,
nn=self.att_net)
self.lin1 = Linear(512, 512)
self.lin2 = Linear(512, 256)
self.lin3 = Linear(256, num_classes)
def forward(self, data):
# Input data
x, edge_index, batch = data.pos, data.edge_index, data.batch
# 1: Conv - ELU - Norm - Pool
x = self.conv1(x, edge_index)
x = F.elu(x)
x = self.norm1(x, batch)
x, edge_index, _, batch, _, _ = self.pool1(x=x, edge_index=edge_index, batch=batch)
# 2: Conv - ELU - Norm - Pool
x = self.conv2(x, edge_index)
x = F.elu(x)
x = self.norm2(x, batch)
x, edge_index, _, batch, _, _ = self.pool2(x=x, edge_index=edge_index, batch=batch)
# 3: Conv - ELU - Norm
x = self.conv3(x, edge_index)
x = F.elu(x)
x = self.norm3(x, batch)
# Global pooling
##x = global_max_pool(x, batch)
x = self.att(x, batch)
# Dense layers
x = F.dropout(x, p=0.5, training=self.training)
x = F.elu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = F.elu(self.lin2(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
return F.log_softmax(x, dim=1)
class PoolNetv3(torch.nn.Module):
def __init__(self, num_classes):
super(PoolNetv3, self).__init__()
self.att_w = nn.Sequential(
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 1)
)
self.att_net = nn.Sequential(
nn.Linear(256, 256),
#nn.Tanh()
nn.ELU()
)
self.conv1 = GATConv(3, 32, heads=2)
self.norm1 = InstanceNorm(64, affine=True)
self.pool1 = TopKPooling(64, ratio=0.3, nonlinearity=torch.tanh)
self.conv2 = GATConv(64, 128, heads=2)
self.norm2 = InstanceNorm(256, affine=True)
self.att = GlobalAttention(gate_nn=self.att_w,
nn=self.att_net)
self.lin1 = Linear(256, 128)
self.lin2 = Linear(128, 64)
self.lin3 = Linear(64, num_classes)
def forward(self, data):
# Input data
x, edge_index, batch = data.pos, data.edge_index, data.batch
# 1: Conv - ELU - Norm - Pool
x = self.conv1(x, edge_index)
x = F.elu(x)
x = self.norm1(x, batch)
x, edge_index, _, batch, _, _ = self.pool1(x=x, edge_index=edge_index, batch=batch)
# 2: Conv - ELU - Norm - Pool
x = self.conv2(x, edge_index)
x = F.elu(x)
x = self.norm2(x, batch)
# Global pooling
##x = global_max_pool(x, batch)
x = self.att(x, batch)
# Dense layers
x = F.dropout(x, p=0.5, training=self.training)
x = F.elu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = F.elu(self.lin2(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
return F.log_softmax(x, dim=1)
if __name__ == "__main__":
from data import get_data
from torch_geometric.data import DataLoader
from tqdm.auto import tqdm
config = {'BATCH_SIZE' : 16,
'NUM_EPOCHS' : 1600,
'LEARNING_RATE': 0.001,
'STEP_DECAY': 1000,
'GAMMA_DECAY': 0.9,
'DATA_DIR': 'frgc',
'RANDOM_SEED': 23,
'NUM_SPLITS': 3,
'FILTER_LOWER_THAN': 20,
'FILTER_GREATER_THAN': -1,
'DEVICE': 'cuda:0',
'TOT_TRAIN_PCTG': 0.7,
'FOLD_TRAIN_PCTG': 0.67,
'NET_TYPE': "pool2",
'NORM_VERT': True,
'USE_COO': True}
device = config['DEVICE']
# Data ------------------------------------------------------------------------
train_list, classes_train, test_list, classes_test = get_data(config)
num_classes = len(torch.unique(torch.stack(classes_train)))
net = PoolNet(num_classes=num_classes)
test_loader = DataLoader(test_list,
batch_size=config['BATCH_SIZE'],
shuffle=False, num_workers=4)
acc = 0.
for i, data in enumerate(tqdm(test_loader, desc='Test', ncols=100)):
print(16*'-'+str(i)+16*'-')
out = net(data)
pred = torch.argmax(out, dim=1)
acc += pred.eq(data.y).sum().item() / data.num_graphs
break
print(100 * acc / len(test_loader))