File size: 4,489 Bytes
da6986a | 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 | """HalfEdge GNN model definition (moved from brep_two_pass/scripts/03_infer_halfedge.py)."""
from __future__ import annotations
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import (
HeteroConv, SAGEConv, JumpingKnowledge, BatchNorm,
global_mean_pool, global_max_pool,
)
from torch_geometric.data import HeteroData
def resolve_reject_label(labels, requested: Optional[str]) -> Optional[str]:
if requested:
return requested
lower_to_name = {name.lower(): name for name in labels}
for candidate in ("random", "unknown", "other", "misc"):
if candidate in lower_to_name:
return lower_to_name[candidate]
for name in labels:
lower = name.lower()
if "random" in lower or "unknown" in lower:
return name
return None
class HalfEdgeGNN(nn.Module):
def __init__(
self,
coedge_in: int, face_in: int, edge_in: int, global_in: int,
hidden=256, layers=6, dropout=0.2, num_classes=3,
jk_mode="cat", gating_dim=None,
):
super().__init__()
self.convs = nn.ModuleList(); self.bns = nn.ModuleList()
self.encoders = nn.ModuleDict({
"coedge": nn.Sequential(nn.Linear(coedge_in, hidden), nn.ReLU(), nn.Dropout(dropout)),
"face": nn.Sequential(nn.Linear(face_in, hidden), nn.ReLU(), nn.Dropout(dropout)),
"edge": nn.Sequential(nn.Linear(edge_in, hidden), nn.ReLU(), nn.Dropout(dropout)),
})
for _ in range(layers):
conv = HeteroConv({
('coedge','next','coedge'): SAGEConv((hidden, hidden), hidden),
('coedge','prev','coedge'): SAGEConv((hidden, hidden), hidden),
('coedge','mate','coedge'): SAGEConv((hidden, hidden), hidden),
('coedge','to_face','face'): SAGEConv((hidden, hidden), hidden),
('face','to_coedge','coedge'): SAGEConv((hidden, hidden), hidden),
('coedge','to_edge','edge'): SAGEConv((hidden, hidden), hidden),
('edge','to_coedge','coedge'): SAGEConv((hidden, hidden), hidden),
('face','to_edge','edge'): SAGEConv((hidden, hidden), hidden),
('edge','to_face','face'): SAGEConv((hidden, hidden), hidden),
}, aggr='sum')
self.convs.append(conv)
self.bns.append(nn.ModuleDict({
"coedge": BatchNorm(hidden),
"face": BatchNorm(hidden),
"edge": BatchNorm(hidden),
}))
self.jk = JumpingKnowledge(mode=jk_mode)
self.jk_out = hidden * layers if jk_mode == "cat" else hidden
if gating_dim is None:
gating_dim = hidden
self.gating_dim = gating_dim
self.pool_in = self.jk_out * 2
self.proj = nn.Identity() if self.pool_in == gating_dim else nn.Linear(self.pool_in, gating_dim)
self.global_mlp = nn.Sequential(
nn.Linear(global_in, gating_dim),
nn.ReLU(), nn.Dropout(0.3),
nn.Linear(gating_dim, 2 * gating_dim),
)
self.head = nn.Sequential(
nn.Linear(gating_dim, hidden),
nn.ReLU(), nn.Dropout(dropout),
nn.Linear(hidden, num_classes),
)
def forward(self, data: HeteroData):
x = {
"coedge": self.encoders["coedge"](data["coedge"].x),
"face": self.encoders["face"](data["face"].x),
"edge": self.encoders["edge"](data["edge"].x),
}
outs = []
for conv, bn in zip(self.convs, self.bns):
x_new = conv(x, data.edge_index_dict)
x = {k: F.relu(bn[k](x_new[k]) + x[k]) for k in x}
outs.append(x["coedge"])
xj = self.jk(outs)
batch = data['coedge'].batch
g_mean = global_mean_pool(xj, batch)
g_max = global_max_pool(xj, batch)
g = torch.cat([g_mean, g_max], dim=-1)
g0 = self.proj(g)
global_x = data["global"].x
if global_x.dim() == 1:
global_x = global_x.view(1, -1)
if global_x.size(0) != g0.size(0):
raise RuntimeError(f"Global feature batch mismatch: {global_x.size(0)} vs {g0.size(0)}")
gb = self.global_mlp(global_x)
gamma, beta = gb.chunk(2, dim=-1)
gamma = torch.sigmoid(gamma)
g_mod = g0 * gamma + beta
return self.head(g_mod)
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