File size: 2,772 Bytes
ff0fadf | 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 | import torch
import torch.nn as nn
import torch_geometric.nn as nng
from onescience.modules.mlp.MLP import StandardMLP
class Model(nn.Module):
"""
GraphSAGE 模型。
使用 SAGEConv 进行邻居聚合，并结合 MLP 进行特征编码和解码。
"""
def __init__(self, args, device):
super(Model, self).__init__()
self.__name__ = "GraphSAGE"
self.nb_hidden_layers = args.n_layers
self.size_hidden_layers = args.n_hidden
self.bn_bool = True
self.activation = nn.ReLU()
self.encoder = StandardMLP(
input_dim=args.fun_dim + args.space_dim,
output_dim=args.n_hidden,
hidden_dims=[args.n_hidden * 2],
activation=args.act,
use_bias=True
)
self.decoder = StandardMLP(
input_dim=args.n_hidden,
output_dim=args.out_dim,
hidden_dims=[args.n_hidden * 2],
activation=args.act,
use_bias=True
)
# Graph Layers (Keep PyG implementation for consistency)
self.in_layer = nng.SAGEConv(
in_channels=args.n_hidden, out_channels=self.size_hidden_layers
)
self.hidden_layers = nn.ModuleList()
for n in range(self.nb_hidden_layers - 1):
self.hidden_layers.append(
nng.SAGEConv(
in_channels=self.size_hidden_layers,
out_channels=self.size_hidden_layers,
)
)
self.out_layer = nng.SAGEConv(
in_channels=self.size_hidden_layers, out_channels=self.size_hidden_layers
)
if self.bn_bool:
self.bn = nn.ModuleList()
for n in range(self.nb_hidden_layers):
self.bn.append(
nn.BatchNorm1d(self.size_hidden_layers, track_running_stats=False)
)
def forward(self, x, fx, T=None, geo=None):
if x.dim() == 3:
x = x.squeeze(0) # [1, N, C] → [N, C]
if fx is not None and fx.dim() == 3:
fx = fx.squeeze(0) # [1, N, C] → [N, C]
if geo.dim() == 3:
edge_index = geo.squeeze(0) # [1, 2, E] → [2, E]
else:
edge_index = geo
z = torch.cat((x, fx), dim=-1)
z = self.encoder(z)
z = self.in_layer(z, edge_index)
if self.bn_bool:
z = self.bn[0](z)
z = self.activation(z)
for n in range(self.nb_hidden_layers - 1):
z = self.hidden_layers[n](z, edge_index)
if self.bn_bool:
z = self.bn[n + 1](z)
z = self.activation(z)
z = self.out_layer(z, edge_index)
z = self.decoder(z)
return z.unsqueeze(0)
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