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4d0da28 3de59dd 4d0da28 3de59dd 4d0da28 3de59dd 4d0da28 | 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 | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from torch_geometric.nn import BatchNorm, global_add_pool, AttentiveFP, Set2Set
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear as GeometricLinear
from torch_geometric.utils import degree, scatter
from torch.nn import ModuleList, Linear, ReLU, Sequential, Dropout
class LegacyPNAConv(MessagePassing):
"""PyG 2.0.1 PNA behavior used to train the bundled checkpoint."""
def __init__(self, in_channels, out_channels, aggregators, scalers, deg,
edge_dim=None, towers=1, pre_layers=1, post_layers=1,
divide_input=False, **kwargs):
kwargs.setdefault('aggr', None)
super().__init__(node_dim=0, **kwargs)
if divide_input:
assert in_channels % towers == 0
assert out_channels % towers == 0
self.in_channels = in_channels
self.out_channels = out_channels
self.aggregators = aggregators
self.scalers = scalers
self.edge_dim = edge_dim
self.towers = towers
self.divide_input = divide_input
self.F_in = in_channels // towers if divide_input else in_channels
self.F_out = out_channels // towers
deg = deg.to(torch.float)
self.avg_deg = {
'lin': deg.mean().item(),
'log': (deg + 1).log().mean().item(),
'exp': deg.exp().mean().item(),
}
if edge_dim is not None:
self.edge_encoder = GeometricLinear(edge_dim, self.F_in)
self.pre_nns = ModuleList()
self.post_nns = ModuleList()
for _ in range(towers):
modules = [GeometricLinear((3 if edge_dim else 2) * self.F_in, self.F_in)]
for _ in range(pre_layers - 1):
modules += [ReLU(), GeometricLinear(self.F_in, self.F_in)]
self.pre_nns.append(Sequential(*modules))
post_in = (len(aggregators) * len(scalers) + 1) * self.F_in
modules = [GeometricLinear(post_in, self.F_out)]
for _ in range(post_layers - 1):
modules += [ReLU(), GeometricLinear(self.F_out, self.F_out)]
self.post_nns.append(Sequential(*modules))
self.lin = GeometricLinear(out_channels, out_channels)
self.reset_parameters()
def reset_parameters(self):
if self.edge_dim is not None:
self.edge_encoder.reset_parameters()
for network in self.pre_nns:
for module in network:
if hasattr(module, 'reset_parameters'):
module.reset_parameters()
for network in self.post_nns:
for module in network:
if hasattr(module, 'reset_parameters'):
module.reset_parameters()
self.lin.reset_parameters()
def forward(self, x, edge_index, edge_attr=None):
if self.divide_input:
x = x.view(-1, self.towers, self.F_in)
else:
x = x.view(-1, 1, self.F_in).repeat(1, self.towers, 1)
out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=None)
out = torch.cat([x, out], dim=-1)
out = torch.cat([network(out[:, i]) for i, network in enumerate(self.post_nns)], dim=1)
return self.lin(out)
def message(self, x_i, x_j, edge_attr):
if edge_attr is not None:
edge_attr = self.edge_encoder(edge_attr)
edge_attr = edge_attr.view(-1, 1, self.F_in).repeat(1, self.towers, 1)
features = torch.cat([x_i, x_j, edge_attr], dim=-1)
else:
features = torch.cat([x_i, x_j], dim=-1)
return torch.stack([network(features[:, i]) for i, network in enumerate(self.pre_nns)], dim=1)
def aggregate(self, inputs, index, dim_size=None):
outputs = []
for aggregator in self.aggregators:
if aggregator in {'sum', 'mean', 'min', 'max'}:
output = scatter(inputs, index, dim=0, dim_size=dim_size, reduce=aggregator)
elif aggregator in {'var', 'std'}:
mean = scatter(inputs, index, dim=0, dim_size=dim_size, reduce='mean')
mean_squares = scatter(inputs * inputs, index, dim=0, dim_size=dim_size, reduce='mean')
output = mean_squares - mean * mean
if aggregator == 'std':
output = torch.sqrt(torch.relu(output) + 1e-5)
else:
raise ValueError(f'Unknown aggregator "{aggregator}"')
outputs.append(output)
output = torch.cat(outputs, dim=-1)
node_degree = degree(index, dim_size, dtype=inputs.dtype).clamp_(1).view(-1, 1, 1)
outputs = []
for scaler in self.scalers:
if scaler == 'identity':
pass
elif scaler == 'amplification':
output = output * (torch.log(node_degree + 1) / self.avg_deg['log'])
elif scaler == 'attenuation':
output = output * (self.avg_deg['log'] / torch.log(node_degree + 1))
elif scaler == 'linear':
output = output * (node_degree / self.avg_deg['lin'])
elif scaler == 'inverse_linear':
output = output * (self.avg_deg['lin'] / node_degree)
else:
raise ValueError(f'Unknown scaler "{scaler}"')
outputs.append(output)
return torch.cat(outputs, dim=-1)
class MLP(nn.Module):
def __init__(self,dims, n_layers, hidden_size, dropout=0 ):
super().__init__()
self.n_layers = n_layers
self.hidden_size = hidden_size
self.dims = dims
self.dropout = dropout
def block(in_size, n_hidden):
layers = [
nn.Linear(in_size, n_hidden),
nn.BatchNorm1d(n_hidden),
nn.ReLU(),
]
if self.dropout > 0:
layers.append(
nn.Dropout(self.dropout),
)
return layers
# Define PyTorch model
self.model = nn.Sequential(
*block(np.prod(dims), self.hidden_size)
)
self.latent_size = self.hidden_size
def forward(self, x):
return self.model(x)
class GNN_PNAConv(torch.nn.Module):
def __init__(self,
nodes_n_features: int,
edges_n_features: int,
deg: torch.tensor ,
n_layers: int = 6,
hidden_size_node: int = 75,
hidden_size_edges: int = 50,
towers: int = 5,
fcc_hidden_size: int = 50,
dropout: float = 0,
use_fds: bool = False,
**args
):
super(GNN_PNAConv, self).__init__()
self.node_emb = MLP(nodes_n_features, 1, hidden_size_node)
self.edge_emb = MLP(edges_n_features, 1, hidden_size_edges)
aggregators = ['mean', 'min', 'max', 'std']
scalers = ['identity', 'amplification', 'attenuation']
self.convs = ModuleList()
self.batch_norms = ModuleList()
for _ in range(n_layers):
conv = LegacyPNAConv(in_channels=hidden_size_node, out_channels=hidden_size_node,
aggregators=aggregators, scalers=scalers, deg=deg,
edge_dim=hidden_size_edges, towers=towers, pre_layers=2, post_layers=2,
divide_input=False)
self.convs.append(conv)
self.batch_norms.append(BatchNorm(hidden_size_node))
self.set2set = Set2Set(hidden_size_node, processing_steps=6)
fc_layers = [
Linear(2*hidden_size_node, hidden_size_node), BatchNorm(hidden_size_node), ReLU(),
Linear(hidden_size_node, fcc_hidden_size), BatchNorm(fcc_hidden_size), ReLU(),
]
self.fcc_hidden_size = fcc_hidden_size
if dropout>0:
fc_layers += [ Dropout(p=dropout) ]
self.mlp = Sequential( *fc_layers )
def forward(self, x, edge_index, edge_attr, batch):
x = self.node_emb(x.squeeze())
edge_attr = self.edge_emb(edge_attr)
for conv, batch_norm in zip(self.convs, self.batch_norms):
x = F.relu(batch_norm(conv(x, edge_index, edge_attr)))
x = self.set2set(x, batch) #Set2Set #GlobalAttention #GraphMultisetTransformer
return self.mlp(x)
class QdolarAR(torch.nn.Module):
def __init__(self,
nodes_n_features: int,
*args, **kargs
):
super(QdolarAR, self).__init__()
n_layers: int = 5
hidden_size_node: int = nodes_n_features
fcc_hidden_size: int = 100
dropout: float = 0.28
self.fcc_hidden_size = fcc_hidden_size
self.dropout = dropout
self.n_layers = n_layers
self.hidden_size_node = hidden_size_node
assert n_layers >=3
fc_layers = [
BatchNorm(hidden_size_node),
Linear( hidden_size_node, fcc_hidden_size), BatchNorm(fcc_hidden_size), ReLU(),
]
for i in range(n_layers-2):
fc_layers += [
Linear(fcc_hidden_size, fcc_hidden_size), BatchNorm(fcc_hidden_size), ReLU(),
]
if dropout>0:
fc_layers += [ Dropout(p=dropout) ]
self.mlp = Sequential( *fc_layers )
def forward(self, x, edge_index, edge_attr, batch):
assert len(batch) == len(batch.unique())
assert not torch.isnan(x).any(), "Error, x contains nan"
return self.mlp(x)
class GNN_AttentiveFP(torch.nn.Module):
def __init__(self,
nodes_n_features: int,
edges_n_features: int,
deg: torch.tensor ,
n_layers: int = 4,
hidden_size_node: int = 75,
hidden_size_edges: int = 50,
towers: int = 5,
fcc_hidden_size: int = 50,
dropout: float = 0,
use_fds: bool = False,
**args
):
super(GNN_AttentiveFP, self).__init__()
self.attent_net = AttentiveFP(nodes_n_features, hidden_size_node, hidden_size_node, edges_n_features,
n_layers, n_layers, dropout= dropout)
fc_layers = [
Linear(hidden_size_node, hidden_size_edges), ReLU(),
Linear(hidden_size_edges, fcc_hidden_size), ReLU(),
]
self.fcc_hidden_size = fcc_hidden_size
if dropout>0:
fc_layers += Dropout(p=dropout)
self.mlp = Sequential( *fc_layers )
def forward(self, x, edge_index, edge_attr, batch):
x = self.attent_net(x, edge_index, edge_attr, batch)
return self.mlp(x)
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