mirror of
https://github.com/unanmed/ginka-generator.git
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56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch_geometric.nn import GATConv, AttentionalAggregation, global_max_pool
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from torch_geometric.data import Data
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class MinamoTopoModel(nn.Module):
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def __init__(
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self, tile_types=32, emb_dim=128, hidden_dim=128, out_dim=512, mlp_dim=512
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):
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super().__init__()
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# 传入 softmax 概率值，直接映射
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self.input_proj = nn.Linear(tile_types, emb_dim)
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# 图卷积层
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self.conv1 = GATConv(emb_dim, hidden_dim*2, heads=8, dropout=0.2)
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self.conv2 = GATConv(hidden_dim*16, hidden_dim*2, heads=8)
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self.conv3 = GATConv(hidden_dim*16, hidden_dim*2, heads=8)
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self.conv4 = GATConv(hidden_dim*16, out_dim, heads=1)
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# 正则化
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self.norm1 = nn.LayerNorm(hidden_dim*16)
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self.norm2 = nn.LayerNorm(hidden_dim*16)
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self.norm3 = nn.LayerNorm(hidden_dim*16)
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self.norm4 = nn.LayerNorm(out_dim)
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self.drop = nn.Dropout(0.3)
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# 增强MLP
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self.fc = nn.Sequential(
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nn.Linear(out_dim, mlp_dim),
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)
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def forward(self, graph: Data):
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x = self.input_proj(graph.x)
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x = self.conv1(x, graph.edge_index)
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x = F.relu(self.norm1(x))
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x = self.conv2(x, graph.edge_index)
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x = F.relu(self.norm2(x))
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x = self.conv3(x, graph.edge_index)
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x = F.relu(self.norm3(x))
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x = self.conv4(x, graph.edge_index)
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x = F.relu(self.norm4(x))
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# 池化
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x = self.drop(x)
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x = global_max_pool(x, graph.batch)
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topo_vec = self.fc(x)
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# 归一化
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return F.normalize(topo_vec, p=2, dim=-1)
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