-
Notifications
You must be signed in to change notification settings - Fork 186
/
generalconv.py
242 lines (204 loc) · 8.72 KB
/
generalconv.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
import torch
import torch.nn as nn
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.inits import glorot, zeros
from torch_geometric.utils import add_remaining_self_loops
from torch_scatter import scatter_add
from graphgym.config import cfg
class GeneralConvLayer(MessagePassing):
r"""General GNN layer
"""
def __init__(self,
in_channels,
out_channels,
improved=False,
cached=False,
bias=True,
**kwargs):
super(GeneralConvLayer, self).__init__(aggr=cfg.gnn.agg, **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.weight = Parameter(torch.Tensor(in_channels, out_channels))
if cfg.gnn.self_msg == 'concat':
self.weight_self = Parameter(
torch.Tensor(in_channels, out_channels))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
glorot(self.weight)
if cfg.gnn.self_msg == 'concat':
glorot(self.weight_self)
zeros(self.bias)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index,
num_nodes,
edge_weight=None,
improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ),
dtype=dtype,
device=edge_index.device)
fill_value = 1.0 if not improved else 2.0
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
def forward(self, x, edge_index, edge_weight=None, edge_feature=None):
""""""
if cfg.gnn.self_msg == 'concat':
x_self = torch.matmul(x, self.weight_self)
x = torch.matmul(x, self.weight)
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
x_msg = self.propagate(edge_index,
x=x,
norm=norm,
edge_feature=edge_feature)
if cfg.gnn.self_msg == 'none':
return x_msg
elif cfg.gnn.self_msg == 'add':
return x_msg + x
elif cfg.gnn.self_msg == 'concat':
return x_msg + x_self
else:
raise ValueError('self_msg {} not defined'.format(
cfg.gnn.self_msg))
def message(self, x_j, norm, edge_feature):
if edge_feature is None:
return norm.view(-1, 1) * x_j if norm is not None else x_j
else:
return norm.view(-1, 1) * (
x_j + edge_feature) if norm is not None else (x_j +
edge_feature)
def update(self, aggr_out):
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)
class GeneralEdgeConvLayer(MessagePassing):
r"""General GNN layer, with edge features
"""
def __init__(self,
in_channels,
out_channels,
improved=False,
cached=False,
bias=True,
**kwargs):
super(GeneralEdgeConvLayer, self).__init__(aggr=cfg.gnn.agg, **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.msg_direction = cfg.gnn.msg_direction
if self.msg_direction == 'single':
self.linear_msg = nn.Linear(in_channels + cfg.dataset.edge_dim,
out_channels,
bias=False)
else:
self.linear_msg = nn.Linear(in_channels * 2 + cfg.dataset.edge_dim,
out_channels,
bias=False)
if cfg.gnn.self_msg == 'concat':
self.linear_self = nn.Linear(in_channels, out_channels, bias=False)
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
zeros(self.bias)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index,
num_nodes,
edge_weight=None,
improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ),
dtype=dtype,
device=edge_index.device)
fill_value = 1.0 if not improved else 2.0
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
def forward(self, x, edge_index, edge_weight=None, edge_feature=None):
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
x_msg = self.propagate(edge_index,
x=x,
norm=norm,
edge_feature=edge_feature)
if cfg.gnn.self_msg == 'concat':
x_self = self.linear_self(x)
return x_self + x_msg
elif cfg.gnn.self_msg == 'add':
return x + x_msg
else:
return x_msg
def message(self, x_i, x_j, norm, edge_feature):
if self.msg_direction == 'both':
x_j = torch.cat((x_i, x_j, edge_feature), dim=-1)
else:
x_j = torch.cat((x_j, edge_feature), dim=-1)
x_j = self.linear_msg(x_j)
return norm.view(-1, 1) * x_j if norm is not None else x_j
def update(self, aggr_out):
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)